The authors have formulated a Comprehensive Systems Engineering approach to Electro-Mechanical Actuator (EMA) Prognostics and Health Management (PHM) system development. The approach implements software tools to integrate simulation-based design principles and dynamic failure mode and effects analysis. It also provides automated failure mode insertion and propagation analysis, PHM algorithm design and verification, full dynamic simulations, code generation, and validation testing. This process aims to produce the appropriate fault detection and prediction algorithms needed for successful development of an EMA PHM system. 

As an initial use case, the developed approach was implemented to develop and validate a model-based, virtual sensor software package for landing gear EMA PHM. This effort included creation of a dynamic, component-level system model that can be used to virtually sense parameters, detect degradation, isolate probable root cause, and assess severity. This model is also used as a virtual test bed for performing fault insertion analysis to address algorithm development and experimental prioritization. The developed model was validated using data from a test stand, which was specifically constructed for EMA PHM development. The model-based predictor was then coupled with failure mode diagnostics, advanced knowledge fusion, and failure mode progression algorithms to form a complete prototype EMA PHM solution.

Reproduced by kind permission of MFPT (www.mfpt.org).
Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance.  Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a
specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the 
least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.
Being relatively new to the field, electromechanical actuators in aerospace applications lack the knowledge base compared to ones accumulated for the other actuator types, especially when it comes to fault detection and characterization. Lack of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests push for the need of building system models and designing affordable but realistic experimental setups. This paper presents our approach to accomplish a comprehensive test environment equipped with fault injection and data collection capabilities. Efforts also include development of multiple models for EMA operations, both in nominal and fault conditions that can be used along with measurement data to generate effective diagnostic and prognostic estimates. A detailed description has been provided about how various failure modes are inserted in the test environment and corresponding data is collected to verify the physics based models under these failure modes that have been developed in parallel. A design of experiment study has been included to outline the details of experimental data collection. Furthermore, some ideas about how experimental results can be extended to real flight environments through actual flight tests and using real flight data have been presented. Finally, the roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators is discussed.*
Electro-mechanical actuators (EMA) are finding increasing use in aerospace applications, especially with the trend towards all all-electric aircraft and spacecraft designs. However, electro-mechanical actuators still lack the knowledge base accumulated for other fielded actuator types, particularly with regard to fault detection and characterization. This paper presents a thorough analysis of some of the critical failure modes documented for EMAs and describes experiments conducted on detecting and isolating a subset of them. The list of failures has been prepared through an extensive Failure Modes and Criticality Analysis (FMECA) reference, literature review, and accessible industry experience. Methods for data acquisition and validation of algorithms on EMA test stands are described. A variety of condition indicators were developed that enabled detection, identification, and isolation among the various fault modes. A diagnostic algorithm based on an artificial neural network is shown to operate successfully using these condition indicators and furthermore, robustness of these diagnostic routines to sensor faults is demonstrated by showing their ability to distinguish between them and component failures. The paper concludes with a roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators.
Sensor faults continue to be a major hurdle for sys- tems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally dif- ficult to seed particular sensor faults. Therefore, research is un- derway to better understand the different fault modes seen in sen- sors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper il- lustrates the work with data collected from an electromechanical actuator in an aerospace setting, equipped with temperature, vi- bration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network-based classi- fier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and dis- ambiguation efficacy with respect to severity of fault conditions.
This is the Coarse Mixed Element Grid for the RSW with a viscous wall at the root.  This grid is for a node-based unstructured solver.

Quad Surface Faces=      9728
Tria Surface Faces=    100398
Nodes             =   2859048
Total Elements    =   6775906
Hex Elements      =         0
Pent_5 Elements   =     13903
Pent_6 Elements   =   5086383
Tet Elements      =   1675620
BL Tet Elements   =     13675
This is the RSW Coarse Tet Only grid with the root viscous tunnel wall.  This grid is for a node-based unstructured solver.

Quad Surface Faces=         0
Tria Surface Faces=    119854
Nodes             =   2861927
Total Elements    =  16981322
Hex Elements      =         0
Pent_5 Elements   =         0
Pent_6 Elements   =         0
Tet Elements      =  16981322
BL Tet Elements   =  15310361
RSW Medium Mixed Element Grid with Viscous Wind Tunnel Wall at the root. This grid is for a node-based unstructured solver.

Quad Surface Faces=     18432
Tria Surface Faces=    216952
Nodes             =   8110917
Total Elements    =  21779887
Hex Elements      =         0
Pent_5 Elements   =     39232
Pent_6 Elements   =  13196220
Tet Elements      =   8544435
BL Tet Elements   =     38412
New Medium Fully Tetrahedral RSW Grid with viscous wind tunnel wall at the root.  This grid is for a node-based unstructured solver.

Medium Tet:
Quad Surface Faces=         0
Tria Surface Faces=    253816
Nodes             =   8114050
Total Elements    =  48230386
Hex Elements      =         0
Pent_5 Elements   =         0
Pent_6 Elements   =         0
Tet Elements      =  48230386
BL Tet Elements   =  39714124
NEW RSW Fine Fully Tetrahedral Grid with Viscous Wind Tunnel wall at the root. This grid is for a node-based unstructured solver.  Note that the CGNS file is very large and is gzipped.

Quad Surface Faces=         0
Tria Surface Faces=    553258
Nodes             =  23997526
Total Elements    = 142995542
Hex Elements      =         0
Pent_5 Elements   =         0
Pent_6 Elements   =         0
Tet Elements      = 142995542
BL Tet Elements   = 126168161
RSW Fine Mixed Element Grid with viscous root wind tunnel wall.  This grid is for a node-based unstructured solver.

Quad Surface Faces=     38016
Tria Surface Faces=    477226
Nodes             =  23988100
Total Elements    =  58923530
Hex Elements      =         0
Pent_5 Elements   =     70279
Pent_6 Elements   =  41974373
Tet Elements      =  16878878
BL Tet Elements   =     69729
Chimera medium-size grid for HIRENASD. Two files, fort.501 for grid for wing, fuselage, and 'world' zones, fort.503 for collar zone. File format is plot3d, unstructured, double precision, multi-zone.
Information that was shared at the meetings held in Paris is located in this resource page

<a href="http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6VK2-4TC925R-1-1&_cdi=6110&_user=141910&_pii=S1270963808000783&_origin=search&_zone=rslt_list_item&_coverDate=03%2F31%2F2009&_sk=999869997&wchp=dGLzVlb-zSkzV&md5=be385b276b917ea4c7c982a90fff7586&ie=/sdarticle.pdf">Paper that was published in magazine</a> is linked directly
Corase Grid:
Quad Surface Faces=      9360
Tria Surface Faces=    128928
Nodes             =   2869187
Total Elements    =   9099201
Hex Elements      =         0
Pent_5 Elements   =         0
Pent_6 Elements   =   3915576
Tet Elements      =   5183625
Maximum Element Angle:  172.268836
Minimum Element Angle:  2.048648

Medium Grid:
Quad Surface Faces=     15840
Tria Surface Faces=    293112
Nodes             =   8364319
Total Elements    =  27482638
Hex Elements      =         0
Pent_5 Elements   =         0
Pent_6 Elements   =  11004664
Tet Elements      =  16477974
Maximum Element Angle:  174.936370
Minimum Element Angle:  1.436539

Fine Grid:
Quad Surface Faces=     23848
Tria Surface Faces=    693978
Nodes             =  24098844
Total Elements    =  91819590
Hex Elements      =         0
Pent_5 Elements   =         0
Pent_6 Elements   =  25397592
Tet Elements      =  66421998
Maximum Element Angle:  179.975214
Minimum Element Angle:  0.017963
This is the RSW fully tetrahedral unstructured mesh dataset for a cell-centered code, including the viscous wind tunnel wall.

UG3      : Grid File Name    = rsw_coarse_tetcc.b8.ugrid
UG3      : Quad Surface Faces=         0
UG3      : Tria Surface Faces=     61750
UG3      : Nodes             =   1357828
UG3      : Total Elements    =   8050193
UG3      : Hex Elements      =         0
UG3      : Pent_5 Elements   =         0
UG3      : Pent_6 Elements   =         0
UG3      : Tet Elements      =   8050193
UG3      : BL Tet Elements   =   7507013
This is the RSW mixed element unstructured mesh dataset for a cell-centered code, including the viscous wind tunnel wall.

UG3      : Grid File Name    = rsw_coarse_mixedcc.b8.ugrid
UG3      : Quad Surface Faces=      6660
UG3      : Tria Surface Faces=     48430
UG3      : Nodes             =   1356655
UG3      : Total Elements    =   3050271
UG3      : Hex Elements      =         0
UG3      : Pent_5 Elements   =      8741
UG3      : Pent_6 Elements   =   2491985
UG3      : Tet Elements      =    549545
UG3      : BL Tet Elements   =      8550
This is the RSW Fully tetrahedral medium cell-centered unstructured grid with a viscous wall.

UG3      : Grid File Name    = rsw_med_tetcc.b8.ugrid
UG3      : Quad Surface Faces=         0
UG3      : Tria Surface Faces=    133698
UG3      : Nodes             =   3823266
UG3      : Total Elements    =  22726678
UG3      : Hex Elements      =         0
UG3      : Pent_5 Elements   =         0
UG3      : Pent_6 Elements   =         0
UG3      : Tet Elements      =  22726678
UG3      : BL Tet Elements   =  21106651
This RSW gridset is designed as the medium size mixed element grid for use with cell-centered unstructured meshes.

UG3      : Grid File Name    = rsw_med_mixedcc.b8.ugrid
UG3      : Quad Surface Faces=     13056
UG3      : Tria Surface Faces=    107586
UG3      : Nodes             =   3819936
UG3      : Total Elements    =   8667521
UG3      : Hex Elements      =         0
UG3      : Pent_5 Elements   =     18324
UG3      : Pent_6 Elements   =   7010592
UG3      : Tet Elements      =   1638605
UG3      : BL Tet Elements   =     18000
This is a RSW mixed-element unstructured fine mesh for cell-centered solvers.

UG3      : Grid File Name    = rsw_fine_mixedcc.b8.ugrid
UG3      : Quad Surface Faces=     28968
UG3      : Tria Surface Faces=    239342
UG3      : Nodes             =  12002235
UG3      : Total Elements    =  27584618
UG3      : Hex Elements      =         0
UG3      : Pent_5 Elements   =     50183
UG3      : Pent_6 Elements   =  21941289
UG3      : Tet Elements      =   5593146
UG3      : BL Tet Elements   =     49651
This is the RSW dataset for a fine fully tetrahedral grid designed for a cell-centered unstructured solver.

UG3      : Grid File Name    = rsw_fine_tetcc.b8.ugrid
UG3      : Quad Surface Faces=         0
UG3      : Tria Surface Faces=    297278
UG3      : Nodes             =  12009356
UG3      : Total Elements    =  71561254
UG3      : Hex Elements      =         0
UG3      : Pent_5 Elements   =         0
UG3      : Pent_6 Elements   =         0
UG3      : Tet Elements      =  71561254
UG3      : BL Tet Elements   =  66001229
not available
Location of the HIRENASD Iges file
Unstructured HIRENASD mesh - fine size (46.4 million nodes, 104 million elements) for node centered solvers - 31.05.2011
Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.
The problem of distance-based outlier detection is difficult
to solve efficiently in very large datasets because of potential
quadratic time complexity. We address this problem and
develop sequential and distributed algorithms that are significantly
more efficient than state-of-the-art methods while
still guaranteeing the same outliers. By combining simple
but effective indexing and disk block accessing techniques,
we have developed a sequential algorithm iOrca that is up to
an order-of-magnitude faster than the state-of-the-art. The
indexing scheme is based on sorting the data points in order
of increasing distance from a fixed reference point and
then accessing those points based on this sorted order. To
speed up the basic outlier detection technique, we develop
two distributed algorithms (DOoR and iDOoR) for modern
distributed multi-core clusters of machines, connected
on a ring topology. The first algorithm passes data blocks
from each machine around the ring, incrementally updating
the nearest neighbors of the points passed. By maintaining
a cutoff threshold, it is able to prune a large number
of points in a distributed fashion. The second distributed
algorithm extends this basic idea with the indexing scheme
discussed earlier. In our experiments, both distributed algorithms
exhibit significant improvements compared to the
state-of-the-art distributed methods.
##Eligibility 
Students with accepted papers at CIDU 2011
##Application deadline
September 12, 2011
This tarball contains a AFLR3 stream grid (b8.ugrid), surface grid, info file, mapbc file, as well as 2 images showing the way the walls were split

Grids made by Rajiv Shenoy (sent by Ben Mann)
## Confirmed Speakers from the United States

<ul>
<li>  Ashok N. Srivastava, NASA Ames Research Center 
<li>  Robert Mah, NASA Ames Research Center
<li>  Gary Hunter, NASA Glenn Research Center
<li>  Nagi Gabraeel, Georgia Tech
<li>  Vitali Volovoi, Georgia Tech
<li>  Eric Feron, Georgia Tech
<li>  Pradeep Lall, Auburn University
<li>  Fu-Kuo Chang, Stanford University
<li>  Tim Wilmering, Boeing
<li>  Natarajan Shankar, SRI
<li>  George Vachtesvanos, Impact Technologies
</ul>
#  Advisory Committee

<ul>
<li>  Dr Kota Harinarayana 
<li>  Dr DS Kothari Chair and former Program Director LCA
<li>  Dr AR Upadhya, Director NAL
<li> Mr Shyam Chetty, Head FMCD, NAL
</ul>

#Organizing Committee

<ul>
<li> Dr Satish Chandra, Chairman
<li> Mr Yogesh Kumar, Chairman Technical Committee
<li> Dr MN Sathaynarayana, I/C PR & Logistics
<li> Mr Jitendra Singh, I/C Planning and Reception
<li> Mr Somnarayanan, I/C Transportation & Press
<li> Mrs Gomathy Sankaran, I/C Accommodation
<li> Dr Pashilkar, Mr CM Ananda, Dr Ramesh Sundaram & Mr Vijay Kumar:  I/C Technical Sessions
<li> Mr. K Chandrasekhar, I/C Finance and Accounts
<li> Dr V Upendranath, Convener
</ul>
Dr. Chwalowski,
We just generated a modified version of the coarse node centered grid with split walls. Here the red and green sections you highlighted in the email have been merged.
Please let me know if it is good so we can generate the rest of the grids and upload them.
Thanks,

- Rajiv
This contains the remaining 5 tarballs (5 grids and associated files) for the RSW node centered grids. Each tarball contains a stream-AFLR3 grid, CGNS grid, surface grid, mapbc file, info file, and tags file.

To assemble the fine tet. grid files use the following command

>> cat fine_tet_nc.tar.gz_part1 fine_tet_nc.tar.gz_part2 > fine_tet_nc.tar.gz
New cell centered grids are generated to complement the node-centered ones uploaded. Six tarballs containing the coarse, medium, and fine mixed-element and pure tet. grids are uploaded.
This is a textbook, created example for illustration purposes.

The System takes inputs of Pt, Ps, and Alt, and calculates the Mach number using the Rayleigh Pitot Tube equation if the plane is flying supersonically.  (See Anderson.)  The unit calculates Cd given the Ma and Alt.

For more details, see the NASA TM, also on this website.
not available
RSW hexahedral grids in CGNS (unstructured) and Plot3d (structured) formats generated by ANSYS Germany. Please see AePW_RSW_Grids.pptx below for full description of these grids.
These grids were constructed using VGRID software from NASA Langley.  The grids designed for node based (labeled 'nc') and cell-centered solvers are supplied.  Grids with mixed elements (labeled 'mixed') and tetrahedral elements (labeled 'tets') are supplied.

Each mixed elements tar-file contains grid in aflr3 .ugrid format and the corresponding .cgns file.  Each tetrahedral elements tar-file contains grid in native VGRID format (.cogsg, .bs, .mapbc) and the corresponding .cgns file.

Some files were too large and had to be split into parts.  To combine the parts, use cat filename_part1 filename_part2 > filename.

Grids are in units of 'mm'.  Information about grids, that is number of nodes, elements, etc., is provided in AePW_HIRENASD_Grid_Statistics_VGRID.docx  

Please report any issues with these grids to Pawel.Chwalowski@nasa.gov

Please note that the final grids for Aeroelastic Prediction Workshop are due on November 1.  Therefore these grids still may change based on your initial calculations.

Soon, the mode shapes from NASTRAN interpolated on the surface mesh will be provided for above grids.

Happy Computing!
These grids were constructed using Centaur software at DLR in Germany. The grids designed for node based (labeled 'cv') and cell-centered solvers (labeled 'cc') are supplied. Grids with mixed elements only are supplied.

Each mixed elements tar-file contains grid in TAU software native format '.nc','.bmap'.  The grid was converted into '.cgns' format.  In some instances, the grids in TAU native formats were converted into aflr3 format at NASA Langley '.ugrid','.mapbc' which in turn were converted to the 'ugrid.cgns' formats.


Some files were too large and had to be split into parts. To combine the parts, use cat filename_part1 filename_part2 > filename.

Grids are in units of 'mm'.  

Please report any issues with these grids to Markus.Ritter@dlr.de and Pawel.Chwalowski@nasa.gov
Experimental Data for HIRENASD configuration

Zip files below contain the experimental data for the pressure coefficients, both the static and the forced oscillation test cases.  

Tecplot files include both the ascii data files and the layout files.
Matlab files contain the data in structures.

Please download the documentation found in the Support/Documentation area of this page for descriptions of the file contents, including variable names and calculation methods.
Updates to Website:  (Please add new items at the top of this description with the date of the website change)

May 9, 2012:  Uploaded experimental data in matlab format for HIRENASD

November 8, 2011:  New grids, experimental data for HIRENASD configuration, new FEM for HIRENASD configuration.  (JHeeg)

Oct 13: Uploaded BSCW grids (VGRID) (PChwalowski)

Oct 5:  Added HIRENASD experimental data for test points #159 and #132 (JHeeg, PChwalowski)
Although mixed-membership models have achieved great success in unsupervised learning, they have not been widely applied to classification problems. In this paper, we propose a family of discriminative mixed-membership models for classification by combining unsupervised mixed membership models with multi-class logistic regression. In particular, we propose two variants respectively applicable to text classification based on latent Dirichlet allocation and usual feature vector classification based on mixed membership naive Bayes models. The proposed models allow the number of components in the mixed membership to be different from the number of classes. We propose two variational inference based algorithms for learning the models, including a fast variational inference which is substantially more efficient than mean-field variational approximation. Through extensive experiments on UCI and text classification benchmark datasets, we show that the models are competitive with the state of the art, and can discover components not explicitly captured by the class labels.
These grids were constructed using VGRID software from NASA Langley. The grids designed for node based (labeled 'nc') and cell-centered solvers are supplied. Grids with mixed elements (labeled 'mixed') and tetrahedral elements (labeled 'tets') are supplied.

Each mixed elements tar-file contains grid in aflr3 .ugrid format and the corresponding .cgns file. Each tetrahedral elements tar-file contains grid in native VGRID format (.cogsg, .bs, .mapbc) and the corresponding .cgns file.

Some files were too large and had to be split into parts. To combine the parts, use cat filename_part1 filename_part2 > filename.

Grids are in units of 'inch'. Information about grids, that is number of nodes, elements, etc., is provided in AePW_BSCW_Grid_Statistics_VGRID.docx

Please report any issues with these grids to Pawel.Chwalowski@nasa.gov

Please note that the final grids for Aeroelastic Prediction Workshop are due on November 1. Therefore these grids still may change based on your initial calculations.
not available
These grids were constructed using VGRID software from NASA Langley. The grids designed for node based (labeled 'nc') and cell-centered solvers are supplied. Grids with mixed elements (labeled 'mixed') and tetrahedral elements (labeled 'tets') are supplied.

Each mixed elements tar-file contains grid in aflr3 .ugrid format and the corresponding .cgns file. Each tetrahedral elements tar-file contains grid in native VGRID format (.cogsg, .bs, .mapbc) and the corresponding .cgns file.

Some files were too large and had to be split into parts. To combine the parts, use cat filename_part1 filename_part2 > filename.

Grids are in units of 'inch'. Information about grids, that is number of nodes, elements, etc., is provided in AePW_BSCW_Grid_Statistics_VGRID_Final.docx

Please report any issues with these grids to Pawel.Chwalowski@nasa.gov
These grids were constructed using VGRID software from NASA Langley. The grids designed for node based (labeled 'nc') and cell-centered (labeled 'cc') solvers are supplied. Grids with mixed elements (labeled 'mixed') and tetrahedral elements (labeled 'tets') are supplied.

Each mixed elements tar-file contains grid in aflr3 .ugrid format and the corresponding .cgns file. Each tetrahedral elements tar-file contains grid in native VGRID format (.cogsg, .bc, .mapbc) and the corresponding .cgns file.

Some files were too large and had to be split into parts. To combine the parts, use cat filename_part1 filename_part2 > filename.

Grids are in units of 'mm'. Information about grids, that is number of nodes, elements, etc., is provided in AePW_HIRENASD_Grid_Statistics_VGRID_Final.docx

Please report any issues with these grids to Pawel.Chwalowski@nasa.gov


The mode shapes from NASTRAN interpolated on the surface mesh are available for some of the grids.  These files are in Tecplot format and units of 'meter.'

Please note that the coarse and medium grids for the mixed elements node based solvers are at the bottom of the list.
Three grids: coarse, medium, and fine in Plot3d format.
Updated November 2, 2011
Contains summary information and analysis condition details for the Aeroelastic Prediction Workshop

Information plotted in this package is found in data form on the website, available for downloading.
The HIRENASD data produced by analyzing the experimental data is repeated on this website, for those who can not download the information in the zip format found on the primary Experimental Data page, or who wish to examine the plots of the data online.
Tecplot (ascii) and matlab files are posted here for the Static pressure coefficient data sets. 

To download all of the data in either tecplot format or matlab format, you can go to https://c3.nasa.gov/dashlink/resources/485/

Please consult the documentation found on this page under Support/Documentation for information regarding variable definition, data processing, etc.
OUTDATED information.  This data was replaced April 2012.  The originally posted HIRENASD reduced data contained numerous errors.  

Tecplot (ascii) and matlab files of the frequency response function estimates are found on this page.

and, remember, you can download all of this in one swift zip file download by going to https://c3.nasa.gov/dashlink/resources/485/
which is the link you get to if you follow through the webpage to DOWNLOAD ALL

For information regarding the files below, please download the documentation found below in the Support/Documentation category.
Files are located here, defining the locations of the pressure transducers on the HIRENASD model.  These locations also correspond to the locations that analysts should extract the pressure coefficient data from their results for comparison with experimental data and comparison with other analyses.
This documents the modal results for the final HIRENASD Finite element models TET model with instrumentation, modelcart, balance and OML modified. 

Post your comments below

Frequency comparisons
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/image001_2.png">
<BR>				

Mode shapes showing only the wing for the last column of mode shapes

<TABLE>
<TR><TD>
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_1.jpg" width=200> <br>Mode 1
</TD><TD>
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_2.jpg" width=200> 
<br>Mode 2</TD><TD>
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_3.jpg" width=200> 
<br>Mode 3</TD></TR>
<TR><TD>
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_4.jpg" width=200> 
<br>Mode 4</TD><TD>
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_5.jpg" width=200> 
<br>Mode 5
</TD><TD><img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_6.jpg" width=200> 
<br>Mode 6
</TD>
</TR>
<TR><TD>
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_7.jpg" width=200> 
<br>Mode 7
</TD><TD><img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_8.jpg" width=200> 
<br>Mode 8
</TD><TD><img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_9.jpg" width=200> 
<br>Mode 9
</TD></TR>
<TR><TD>
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_10.jpg" width=200> 
<br>Mode 10
</TD><TD>
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_11.jpg" width=200> 
<br>Mode 11
</TD><TD>
<img src="https://c3.nasa.gov/dashlink/static/media/dataset/hirenasd_aepw_nov2011_mode_12.jpg" width=200> 
<br>Mode 12
</TD></TR>
</TABLE>
Model with Tetrahedral elements includes wing, balance, exciter and modelcart

Some files were too large and had to be split into parts. To combine the parts, use cat filename_part1 filename_part2 > filename.

<img src="https://c3.nasa.gov/dashlink/static/media/dataset/tableoffreq.png" width=400>

A <a href="https://c3.nasa.gov/dashlink/static/media/dataset/TET_nov2011_modeshapes.pdf"">PDF</a> file with figures of the mode shapes is available.
We provide MATLAB binary files (.mat) and comma separated values files of data collected from a pilot study of a plug load management system that allows for the metering and control of individual electrical plug loads. The study included 15 power strips, each containing 4 channels (receptacles), which wirelessly transmitted power consumption data approximately once per second to 3 bridges. The bridges were connected to a building local area network which relayed data to a cloud-based service. Data were archived once per minute with the minimum, mean, and maximum power draw over each one minute interval recorded. The uncontrolled portion of the testing spanned approximately five weeks and established a baseline energy consumption. The controlled portion of the testing employed schedule-based rules for turning off selected loads during non-business hours; it also modified the energy saver policies for certain devices.

Three folders are provided: “matFilesAllChOneDate” provides a MAT-file for each date, each file has all channels; “matFilesOneChAllDates” provides a MAT-file for each channel, each file has all dates; “csvFiles” provides comma separated values files for each date (note that because of data export size limitations, there are 10 csv files for each date). Each folder has the same data; there is no practical difference in content, only the way in which it is organized.
not available
After discussions by the organizing committee, and some research using the RSW grids, a modification has been made on the RSW grids. The inflow boundary has now been moved so that it is located 1000" in front of the model to provide a more accurate simulation of the boundary layer along the wind tunnel wall.
A data set of two-minute scenarios with a large set of motion and load profiles. Each scenario started with a nominal actuator, then, at 1-minute mark the ballscrew jam fault was injected by switching to the faulty actuator. The scenarios are intended for testing diagnostic algorithms. Please note that only the low speed data (current, temperature, position, and load) were collected during this set of experiments.
A brief self composed research article on Amphibious Aircrafts discussing their use, origin and modern day applications along with their advantages and disadvantages over conventional land-based aircrafts.
Frequently asked questions about FLEA (Flyable Electro-Mechanical Actuator) testbed data sets.
The environmental impact of aviation is enormous given the fact that
in the US alone there are nearly 6 million flights per year of commercial aircraft.
This situation has driven numerous policy and procedural measures to help develop
environmentally friendly technologies which are safe and affordable and reduce the
environmental impact of aviation. However, many of these technologies require significant
initial investment in newer aircraft fleets and modifications to existing regulations
which are both long and costly enterprises. We propose to use an anomaly
detection method based on Virtual Sensors to help detect overconsumption of fuel in
aircraft which relies only on the data recorded during flight of most existing commercial
aircraft, thus significantly reducing the cost and complexity of implementing this
method. The Virtual Sensors developed here are ensemble-learning regression models
for detecting the overconsumption of fuel based on instantaneous measurements
of the aircraft state. This approach requires no additional information about standard
operating procedures or other encoded domain knowledge. We present experimental
results on three data sets and compare five different Virtual Sensors algorithms. The
first two data sets are publicly available and consist of a simulated data set from a
flight simulator and a real-world turbine disk.We show the ability to detect anomalies
with high accuracy on these data sets. These sets contain seeded faults, meaning that
they have been deliberately injected into the system. The second data set is from realworld
fleet of 84 jet aircraft where we show the ability to detect fuel overconsumption
which can have a significant environmental and economic impact. To the best of our
knowledge, this is the first study of its kind in the aviation domain.
The space shuttle main engine (SSME) is part of the Main Propnlsion System (MPS) which is an extremely complex system containing several sub-systems and components, each of which must work precisely in order to achieve a successful mission. A critical component under study is the flow control valve (FCV) which controls the pressure of the gaseous hydrogen between the SSME and the external fuel tank. The FCV has received added attention since a Space Shuttle Mission in November 2008, where it was discovered during the mission that an anomaly had occurred in one of the three FCV's. Subsequent inspection revealed that one FCV cracked during ascent. This type of fault is of high criticality because it can lead to potentially catastrophic gaseous hydrogen leakage. A supervised learning method known as Virtual Sensors (VS), and an unsupervised learning method known as the Inductive Monitoring System (IMS) were used to detect anomalies related to the FCV in the MPS. Both algorithms identify the time of the anomaly in a multi-dimensional time series of temperatures, pressures, and control signals related to the FCV. This discovery corroborates the results of the inspection and also reveals the time at which the anomaly likely occurred. The methods were applied to data obtained from the March 2009 launch of Space Shuttle Discovery to determine whether an anomaly occurred in
the same sub-system. According to our models, the FCV SUb-system showed nominal behavior during ascent.
Software Health Management (SWHM) is a new field that is concerned with the development of tools and technologies to enable automated detection, diagnosis, prediction, and mitigation of adverse events due to software anomalies.
Significant effort has been expended in the last several decades in the
development of verification and validation methods for software
intensive systems, but it is becoming increasingly more apparent that this is
not enough to guarantee that a complex software system
meets all safety and reliability requirements. 

Modern software systems can exhibit a variety of failure modes which can go undetected in a verification and validation process.  While standard techniques for error handling, fault detection and isolation
can have significant benefits for many systems, it is becoming increasingly evident that new technologies and methods are necessary for the development of techniques to detect, diagnose, predict, and then mitigate the adverse events due to software that has already undergone significant verification and validation procedures.
These software faults often arise due to the interaction between the software
and the operating environment.
Unanticipated environmental changes lead to software anomalies that may have significant impact on the overall success of the mission.
Because software is ubiquitous, it is not sufficient that errors are
detected only after they occur. Rather, software must be instrumented and
monitored for failures before they happen.
This prognostic capability will yield safer and more dependable systems for the future.  This paper addresses the motivation, needs, and requirements of software health management as a new discipline.

Published in the Proceedings of the IEEE Conference on Space Mission Challenges for Information Technology, Palo Alto, CA, August 2011.
Sparse machine learning has recently 
emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational cost. This paper posits that these methods can be extremely useful for understanding large collections of text documents, without requiring user expertise in machine learning. Our approach relies on three main ingredients: (a) multi-document text summarization and (b) comparative summarization of two corpora, both using 
parse regression or classification; (c) sparse principal components and sparse graphical models for unsupervised analysis and visualization of large text 
corpora. We validate our approach using a corpus of Aviation Safety Reporting System (ASRS) reports and demonstrate that the methods can reveal causal and contributing factors in runway incursions. Furthermore, we show that the methods automatically discover four main tasks that pilots perform during 
flight, which can aid in further understanding the causal and contributing factors to runway incursions and other drivers for aviation safety incidents. 

Citation:  L. El Ghaoui, G. C. Li, V. Duong, V. Pham, A. N. Srivastava, and K. Bhaduri, “Sparse Machine Learning Methods for Understanding Large Text Corpora,” Proceedings of the Conference on Intelligent Data Understanding, 2011.
One of the primary goals of Integrated Vehicle Health Management (IVHM) is to detect, diagnose, predict, and mitigate adverse events during the flight of an aircraft, regardless of the subsystem(s) from which the adverse event arises. To properly address this problem, it is critical to develop technologies that can integrate large, heterogeneous (meaning that they contain both continuous and discrete signals), asynchronous data streams from multiple subsystems in order to detect a potential adverse event, diagnose its cause, predict the effect of that event on the remaining useful life of the vehicle, and then take appropriate steps to mitigate the event if warranted. These data streams may have highly non-Gaussian distributions and can also contain discrete signals such as caution and warning messages which exhibit non-stationary and obey arbitrary noise models. At the aircraft level, a Vehicle-Level Reasoning System (VLRS) can be developed to provide aircraft with at least two significant capabilities:  improvement of aircraft safety due to enhanced monitoring and reasoning about the aircraft’s health state, and also potential cost savings through Condition Based Maintenance (CBM).  Along with the achieving the benefits of CBM, an important challenge facing aviation safety today is safeguarding against system- and component-level failures and malfunctions. 

Citation:  A. N. Srivastava, D. Mylaraswamy, R. Mah, and E. Cooper, “Vehicle Level Reasoning Systems:  Concept and Future Directions,” Society of Automotive Engineers Integrated Vehicle Health Management Book, Ian Jennions, Ed., 2011.
Previous studies on supporting free-form keyword queries over RDBMSs provide users with linked-structures (e.g.,a 
set of joined tuples) that are relevant to a given keyword query. Most of them focus on ranking individual tuples from one table or joins of multiple tables containing a set of keywords. In this paper, we study the problem of keyword search in a data cube with text-rich dimension(s) (so-called text 
cube). The text cube is built on a multidimensional text database, where each row is associated with some text data (a document) and other structural dimensions (attributes). A cell in the text cube aggregates a set of documents with matching attribute values in a subset of dimensions. We define a keyword-based query language and an IR-style relevance model for coring/ranking cells in the text cube. Given a keyword query, our goal is to find the top-k 
most relevant cells. We propose four approaches, inverted-index one-scan, document sorted-scan, bottom-up 
dynamic programming, and search-space 
ordering. The search-space ordering 
algorithm explores only a small portion of the text cube for finding the top-k 
answers, and enables early termination. Extensive experimental studies are conducted to verify the effectiveness and efficiency of the proposed approaches. 

Citation:  B. Ding, B. Zhao, C. X. Lin, J. Han, C. Zhai, A. N. Srivastava, and N. C. Oza, “Efficient Keyword-Based Search for Top-K Cells in Text Cube,” IEEE Transactions on Knowledge and Data Engineering, 2011.
This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data-driven methods. Standard procedures are used by air traffic controllers (ATCs) to guide aircraft, ensure the safety of the airspace, and maximize runway occupancy. Even though standard procedures are used by ATCs, control of the aircraft remains with the pilots, leading to large variability in the flight patterns observed. Two methods for identifying typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure safe separation between aircraft.


IEEE Transactions on Intelligent Transportation Systems, Dec. 2011, 12(4), pp. 1511 - 1524.
Consider a scenario in which the data owner has some private or sensitive data and wants a data miner to access them for studying important patterns without revealing the sensitive information. Privacy-preserving data mining aims to solve this problem by randomly transforming the data prior to their release to the data miners. Previous works only considered the case of linear data perturbations - additive, multiplicative, or a combination of both - for studying the usefulness of the perturbed output. In this paper, we discuss nonlinear data distortion using potentially nonlinear random data transformation and show how it can be useful for privacy-preserving anomaly detection from sensitive data sets. We develop bounds on the expected accuracy of the nonlinear distortion and also quantify privacy by using standard definitions. The highlight of this approach is to allow a user to control the amount of privacy by varying the degree of nonlinearity. We show how our general transformation can be used for anomaly detection in practice for two specific problem instances: a linear model and a popular nonlinear model using the sigmoid function. We also analyze the proposed nonlinear transformation in full generality and then show that, for specific cases, it is distance preserving. A main contribution of this paper is the discussion between the invertibility of a transformation and privacy preservation and the application of these techniques to outlier detection. The experiments conducted on real-life data sets demonstrate the effectiveness of the approach.
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a 
popular technique for modeling the input-output relations of a set of variables under the assumption 
that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process 
regression to large data sets since prediction based on the learned model requires inversion of an 
order n kernel matrix. Approximate solutions for sparse Gaussian Processes have been proposed for sparse problems. However, in almost all cases, these solution techniques are agnostic to the 
input domain and do not preserve the similarity structure in the data. As a result, although these solutions sometimes provide excellent accuracy, the models do not have interpretability. 
Such interpretable sparsity patterns are very important for many applications. We propose a new technique for sparse Gaussian Process regression that allows us to compute a parsimonious model while preserving the interpretability of the sparsity structure in the data. We discuss how the inverse kernel matrix used in Gaussian Process prediction gives valuable domain information 
and then adapt the inverse covariance estimation from Gaussian graphical models to estimate the Gaussian kernel. We solve the optimization problem using the alternating direction method of 
multipliers that is amenable to parallel computation. We demonstrate the performance of our method in terms of accuracy, scalability and interpretability on a climate data set.
As software and software intensive systems are becoming increasingly ubiquitous, the impact of failures can be tremendous. In some industries such as aerospace, medical devices, or automotive, such failures can cost lives or endan- ger mission success. Software faults can arise due to the inter- action between the software, the hardware, and the operating environment. Unanticipated environmental changes lead to software anomalies that may have significant impact on the overall success of the mission. Latent coding errors can at any time during system operation trigger faults despite the fact that usually a significant effort has been expended in verification and validation (V&V) of the software system. Nevertheless, it is becoming increasingly more apparent that pre-deployment V&V is not enough to guarantee that a com- plex software system meets all safety, security, and reliabil- ity requirements. Software Health Management (SWHM) is a new field that is concerned with the development of tools and technologies to enable automated detection, diagnosis, prediction, and mitigation of adverse events due to software anomalies, while the system is in operation. The prognos- tic capability of the SWHM to detect and diagnose failures before they happen will yield safer and more dependable systems for the future. This paper addresses the motivation, needs, and requirements of software health management as a new discipline and motivates the need for SWHM in safety critical applications.
Most complex aerospace systems have many text reports on safety, maintenance, and associated issues. The Aviation Safety Reporting System (ASRS) spans several decades and contains over 700 000 reports. The Aviation Safety Action Plan (ASAP) contains over 12 000 reports from various airlines. Problem categorizations have been developed for both ASRS and ASAP to enable identification of system problems. However, repository volume and complexity make human analysis difficult. Multiple experts are needed, and they often disagree on classifications. Even the same person has classified the same document differently at different times due to evolving experiences. Consistent classification is necessary to support tracking trends in problem categories over time. A decision support system that performs consistent document classification quickly and over large repositories would be useful. We discuss the results of two algorithms we have developed to classify ASRS and ASAP documents. The first is Mariana---a support vector machine (SVM) with simulated annealing, which is used to optimize hyperparameters for the model. The second method is classification built on top of nonnegative matrix factorization (NMF), which attempts to find a model that represents document features that add up in various combinations to form documents. We tested both methods on ASRS and ASAP documents with the latter categorized two different ways. We illustrate the potential of NMF to provide document features that are interpretable and indicative of topics. We also briefly discuss the tool that we have incorporated Mariana into in order to allow human experts to provide feedback on the document categorizations.
These grids were constructed by ANSYS Germany using ICEM CFD.  Please contact Thorsten Hansen, if you have any questions.
Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.
Prognostics technologies determine the health (or damage) state of a component or sub- system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such infor- mation enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics method- ology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the ow of propellant, so failures may have a signi cant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle ltering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.
Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the esti- mated current state distribution of a component and ex- pected profiles of future usage. In general, this requires simulations of the component using the underlying mod- els. In this paper, we develop a simulation-based pre- diction methodology that achieves computational effi- ciency by performing only the minimal number of sim- ulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented trans- form, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved com- putational efficiency without sacrificing prediction accu- racy.
Verification and validation (V&V) has been identified as a critical phase in fielding systems with Integrated Systems Health Management (ISHM) solutions to ensure that the results produced are robust, reliable, and can confidently inform about vehicle and system health status and to support operational and maintenance decisions. Prognostics is a key constituent within ISHM. It faces unique challenges for V&V since it informs about the future behavior of a component or subsystem. In this paper, we present a detailed review of identified barriers and solutions to prognostics V&V, and a novel methodological way for the organization and application of this knowledge. We discuss these issues within the context of a prognostics application for the ground support equipment of space vehicle propellant loading, and identify the significant barriers and adopted solution for this application.
Prognostics is crucial to providing reliable condition-based maintenance decisions. To obtain accurate predictions of component life, a variety of sensors are often needed. However, it is typically difficult to add enough sensors for reliable prognosis, due to system constraints such as cost and weight. Model-based prognostics helps to offset this problem by exploiting domain knowledge about the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. We develop a model-based prognostics methodology using particle filters, and investigate the benefits of a model-based approach when sensor sets are diminished. We apply our approach to a detailed physics- based model of a pneumatic valve, and perform comprehensive simulation experiments to demonstrate the robustness of model-based approaches under limited sensing scenarios using prognostics performance metrics.
Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a physics-based model that is derived from first principles. In most applications, uncertain- ties from a number of sources cause the predic- tions to be inaccurate and imprecise even with accurate models. Therefore, algorithms are em- ployed that help in managing these uncertainties. Particle filters have become a popular choice to solve this problem due to their wide applicability and ease of implementation. We present a gen- eral model-based prognostics methodology using particle filters. In order to provide more accu- rate and precise estimates, and, therefore, more accurate and precise predictions, we investigate the use of fixed-lag filters. We develop a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach. The exper- iments demonstrate the advantages that fixed-lag filters may provide in the context of prognostics, as measured by prognostics performance metrics.
These grids were constructed by ANSYS Germany using ICEM CFD.  Please contact Thorsten Hansen, if you have any questions.
not available
RSW Wing Grid.  Structured single-block grid with viscous tunnel wall.  PLOT3D Format
not available
not available
The final CFL3D Structured grid used for steady and unsteady computations.  The grid required some minor modifications to accommodate the unsteady computations, so it is different from the grid series used for our grid convergence study.  It is close in size to the medium grid.
Excel Spreadsheets For Data Submittal
Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, i.e., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.
A novel general framework for distributed anomaly detection with theoretical performance guarantees is proposed. Our algorithmic approach combines existing anomaly detection procedures with a novel method for computing global statistics using local sufficient statistics. Under a Gaussian assumption, our distributed algorithm is guaranteed to perform as well as its centralized counterpart, a condition we call Ôzero information lossÕ. We further report experimental results on synthetic as well as real-world data to demonstrate the viability of our approach.
As the amount of textual information grows explosively in various kinds of business systems, it becomes more and more desirable to analyze both structured data records and unstructured text data simultaneously. Although online analytical processing (OLAP) techniques have been proven very useful for analyzing and mining structured data, they face challenges in handling text data. On the other hand, probabilistic topic models are among the most effective approaches to latent topic analysis and mining on text data. In this paper, we study a new data model called topic cube to combine OLAP with probabilistic topic modeling and enable OLAP on the dimension of text data in a multidimensional text database. Topic cube extends the traditional data cube to cope with a topic hierarchy and stores probabilistic content measures of text documents learned through a probabilistic topic model. To materialize topic cubes efficiently, we propose two heuristic aggregations to speed up the iterative Expectation-Maximization (EM) algorithm for estimating topic models by leveraging the models learned on component data cells to choose a good starting point for iteration. Experimental results show that these heuristic aggregations are much faster than the baseline method of computing each topic cube from scratch. We also discuss some potential uses of topic cube and show sample experimental results.
This paper describes a method and system for integrating machine learning with planning and data visualization for the management of mobile sensors for Earth science investigations. Data mining identifies discrepancies between previous observations and predictions made by Earth science models. Locations of these discrepancies become interesting targets for future observations. Such targets become goals used by a flight planner to generate the observation activities. The cycle of observation, data analysis and planning is repeated continuously throughout a multi-week Earth science investigation.
This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advancedtechniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.
Broad classes of statistical classification algorithms have beendeveloped and applied successfully to a wide range of real worlddomains. In general, ensuring that the particular classificationalgorithm matches the properties of the data is crucial inproviding results that meet the needs of the particular applicationdomain. One way in which the impact of this algorithm/applicationmatch can be alleviated is by using ensembles of classifiers, wherea variety of classifiers (either different types of classifiers ordifferent instantiations of the same classifier) are pooled before afinal classification decision is made. Intuitively, classifierensembles allow the different needs of a difficult problem to behandled by classifiers suited to those particular needs.Mathematically, classifier ensembles provide an extra degree offreedom in the classical bias/variance tradeoff, allowing solutionsthat would be difficult (if not impossible) to reach with only asingle classifier. Because of these advantages, classifier ensembles have been applied to many difficult real world problems. In this paper, we surveyselect applications of ensemble methods to problems that havehistorically been most representative of the difficulties inclassification. In particular, we survey applications of ensemblemethods to remote sensing, person recognition, one vs. allrecognition, and medicine.
In this paper we propose $\nu$-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In $\nu$-Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one-class Support Vector Machines while reducing both the training time and the test time by 5-20 times.
Structured multiblock grid of HIRENASD wing with medium grid density, about 10 mill grid points, 9.5 mill cells.
Starting from coarse AIAA AEPW structured grid, heavily modified for:
- Improved boundary-layer resolution at the wing-body junction.
- Possibility of defining multigrid levels (4 levels).
- Improved topology suitable for in-house computers.
Learning and building GMDH polynomial neural networks and printing them as 3rd degree polynomial equation.
Release of CertWare was announced 23 Mar 2012 on:

[code.nasa.gov](http://code.nasa.gov)

The announcement points to the Certware project on NASA’s GitHub repository at:

[nasa.github.com/CertWare](http://nasa.github.com/CertWare/)

The project site contains install instructions as an Eclipse feature, various tutorials and resources, and a link to the GitHub source repository.  

CertWare is released under the NASA Open Source Agreement (NOSA).
Please upload your HIRENASD grids here.  Thank you.
Please upload your BSCW grids here.  Thank you.
Please upload your RSW grids here. Thank you.
not available
Draft Agenda, april 16, 2012
These slides were presented at the AePW, April 21-22, 2012. Additional presentations can be found on each of the associated analysis webpages.  Separate pages contain the RSW, BSCW and HIRENASD analysts' presentations.   

Updated May 15, 2012 JH
These slides were presented at the AePW, April 21-22, 2012. They detail the analytical results generated by each of the participating analysis teams for the Rectangular Supercritical Wing (RSW).
These slides were presented at the AePW, April 21-22, 2012.  They detail the analytical results generated by each of the participating analysis teams for the Benchmark Supercritical Wing (BSCW).
These slides were presented at the AePW, April 21-22, 2012. They detail the analytical results generated by each of the participating analysis teams for the HIRENASD.
Items are added to this page that pertain to administrivial matters for AePW-1.  These include the workshop attendees lists, analysts lists, agenda, etc.  (Or at least they will reside here when I finish reorganizing stuff.)
This resource contains the experimental data that was included in tecplot input files but in matlab files.

dba1_cp has all the results is dimensioned (7,2)
<LI>first dimension is 1-7 for each span station
<LI>2nd dimension is 1 for upper surface, 2 for lower surface.
<p>
<LI>dba1_cp(ispan,isurf).x are the x/c locations at span station (ispan) and upper(isurf=1) or lower(isurf=2)
<LI>dba1_cp(ispan,isurf).y are the eta locations at span station (ispan) and upper(isurf=1) or lower(isurf=2)
<LI>dba1_cp(ispan,isurf).cp are the pressures at span station (ispan) and upper(isurf=1) or lower(isurf=2)
<p>

Unsteady CP is dimensioned with 4 columns 
1st column, real
2nd column, imaginary
3rd column, magnitude
4th column, phase, deg

M,Re and other pertinent variables are included as variables and also included in casedata.M, etc
<a href="http://nia-mediasite.nianet.org/NIAmediasite100/Catalog/pages/catalog.aspx?catalogId=3c58920b-1b8d-4163-becd-6354c7352fd9">Link to Aeroservoelasticity Simulation Workshop WEBCAST </a>
Downloadable Presentation Recordings
Repository for information generated after the workshop, such as debriefings, summaries, publication plans etc
Models developed during Phase I of the VVFCS TA2 Program, NNL10AB32T
Location for style sheets etc used in WordPress.  Wordpress is used for the Workshop Presentations display page. Or at least it will be if I can make this work...
Models developed with this Open Source tool.
Link to SRI's site.  
Available for Windows, MacOS, Linux and FreeBSD. Non-commercial use.
Link to SRI's SAL Toolkit.  Binary distributions available for Windows (using Cygwin), MacOS, and Linux
The following SAL model is an abstraction of a module that implements a fault-tolerant mid-value select on asynchronously produced inputs. This is part of a larger system that has both discrete and continuous dynamics, Our goal is to model the full system using Hybrid SAL and we have adapted the timed relational abstraction techique supported by Hybrid SAL to abstract asynchronous sampling of continous signals. This approach will be fully automated in future releases of Hybrid SAL.

The following model (05/14/2012) shows the resulting abstraction, for the aysnchronous mid-value select module and includes proofs of various properties.
In 1973, Daly, Hpokins, and McKenna (from Draper Lab.) presented a fault-tolerant digital clocking system at the FTCS conference. This is probably one of the first published system designs that is intended to tolerate arbitrary, asymmetric faults (i.e., Byzantine faults).

The following SAL models (05/14/2012) are two variant formalizations of this Draper Clock-Synchronization Protocol developed by Ashish Tiwari.
Timed-Triggered Ethernet (or TTEthernet)is a communication infrastructure that enables the use of Ethernet in real-time, distributed systems. TTEthernet is compatible with traditional IEEE 802.3 switched Ethernet standards, and is designed to support dataflows of mixed criticality on a single network. For traffic of the highest criticality, TTEthernet provides a timed-triggered communication service that relies on a fault-tolerant clock-synchronization protocol.

We have developed formal models of parts of the TTEthernet protocols and analyzed safety-critical properties using both SAL and PVS. Related work by Wilfried Steiner is described in the SAL Wiki.
AADL and Error Annex models of SAFEbus, SPIDER, TTP and BRAIN
This is the bird of the skies of the future. The heavy lift cargo aircraft which is currently being developed by me has twice the payload capacity of an Antonov A-225 aircraft and in hypersonic tests can reach speeds upto 5 - 6 Mach.
A supervised learning task involves constructing a mapping from an input data space (normally described by several features) to an output space. A set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output consists of one or more classes to which the corresponding input belongs. For example, we may have data consisting of observations of sunspots.  In a classification learning task, our goal may be to learn to classify sunspots into one of several types.  Each example may correspond to one candidate sunspot with various measurements or just an image.  A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot.  This model can then be used to classify previously unseen sunspots based on the candidate's measurements. In this chapter, we explain several basic classification algorithms.
A fleet is a group of systems (e.g., cars, aircraft) that are designed and manufactured the same way and are intended to be used the same way. For example, a fleet of delivery trucks may consist of one hundred instances of a particular model of truck, each of which is intended for the same type of service—almost the same amount of time and distance driven every day, approximately the same total weight carried, etc. For this reason, one may imagine that data mining for fleet monitoring may merely involve collecting operating data from the multiple systems in the fleet and developing some sort of model, such as a model of normal operation that can be used for anomaly detection. However, one then may realize that each member of the fleet will be unique in some ways—there will be minor variations in manufacturing, quality of parts, and usage. For this reason, the typical machine learning and statis- tics algorithm’s assumption that all the data are independent and identically distributed is not correct. One may realize that data from each system in the fleet must be treated as unique so that one can notice significant changes in the operation of that system.
The sample scenarios provided here are competition scenarios from Diagnostic Problems I and II of DXC'10. The zip file has a spreadsheet (and pdf) that lists the scenarios and relevant fault information. Each scenario is provided in three formats: tab-delimited .txt file, Matlab .mat file, and tab-delimited .scn file. The different formats are provided for your convenience; they have the same data. The scenario (.scn) files are the format read by the DXC framework. See the system catalogs and schematics for additional information.

For those involved in last year's competition, note that DXC'10 Diagnostic Problem I competition data posted last year (dated 20101105) had some problems which were fixed in the DPI sample data set posted here. The battery "AbruptParasiticLoad" scenario (Exp. 1311) was removed as it was found to have different behavior prior to fault injection than nominal scenarios. Exp 1229_015f was too short (224 seconds) and was removed. Several runs caused an issue when a value was expected to be a real but was interpreted as an integer in the framework (1251, 1285, 1316, 1323, and 1330); this has been fixed.

There are no "AbruptParasiticLoad" faults in the DXC11 DPI competition data.
Competition data, including nominal and faulty scenarios, for Diagnostic Problem I of the Third International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the DXC'11 Industrial Track Sample Data resource page for additional documentation, including system catalogs and schematics. There were no DA entries for Diagnostic Problem II so we are withholding the data for use in a future Diagnostic Competition.
The DXC Framework is a collection of programs and APIs for running and evaluating diagnostic algorithms (DAs) under identical experimental conditions. It is complementary to system XML catalogs and data sets.

The Oracle for DXC'11 is the same as DXC'10 and provides a querying capability to the DAs to determine the correct actions for given faults. Because the Oracle uses proprietary NASA software, it is provided only in binary form. Thus the framework is available as a windows binary installer, linux source files + binary oracle, or windows source files + binary oracle. Please see the included READMEs and example DAs for more information.
Results for the DXC'11 Industrial Track, Diagnostic Problem I (no entries for DP II).
We present the third implementation of a framework created jointly by NASA Ames Research Center, Palo Alto Research Center, and Delft University of Technology to compare and evaluate diagnosis algorithms (DAs). This year's competition, DXC'11, introduces a software track in addition to the industrial and synthetic tracks of previous competitions. A total of eleven DAs competed in the three tracks. The paper describes the systems, diagnostic problems of the tracks, fault scenarios, evaluation metrics, participating DAs, results and analysis.
Slides presented at DXC'11 session of 22nd International Workshop on Principles of Diagnosis
A typical resident of a smart home can be an Alzheimer patient that forgets sometimes to complete the activities that he begins. The key point to assist the smart home resident is to model the activities and discover correct realization patterns of activities. To accomplish this task, we apply sensors to provide primary data about realization patterns of actions, operations, plans, goals and generally any objective that the smart home resident may desire to do. In the consequence, by applying fuzzy clustering techniques, we are able to mine sensor data to retrieve the realization patterns of activities, and so the prediction patterns of intentions are recognizable. Comparing the realization patterns with prediction patterns of activities, we would be able to predict the intention of the resident about the activity that the resident considers to realize. In this way, we would be able to provide hypotheses about the resident goals and his possible goal achievement’s defects. Spatiotemporal aspects of daily activities such as movement of objects are surveyed to discover the patterns of activities realized by the smart homes residents. In this research, uncertainty is considered as a property of activity recognition.
Design of a my single person reusable spacecraft. It can carry one person and it has to be dropped from an aircraft at an altitude of 40,000 - 45,000 feet. 
Can be the future solution for cheap public space travel.
Previous research described the use of machine learning algorithms to predict aircraft fuel consumption. This technique, known as Virtual Sensors, models fuel consumption as a function of aircraft Flight Operations Quality Assurance (FOQA) data. FOQA data consist of a large number of measurements that are already recorded by many commercial airlines. The predictive model is used for anomaly detection in the fuel consumption history by noting when measured fuel consumption exceeds an expected value. This exceedance may indicate overconsumption of fuel, the source of which may be identified and corrected by the aircraft operator. This would reduce both fuel emissions and operational costs. This paper gives a brief overview of the modeling approach and describes efforts to validate and analyze the initial results of this project. We examine the typical error in modeling, and compare modeling accuracy against both complex and  simplistic regression approaches. We also estimate a ranking of the importance of each FOQA variable used as input, and demonstrate that FOQA variables can reliably be used to identify different modes of fuel consumption, which may be useful in future work. Analysis indicates that fuel consumption is accurately predicted while remaining theoretically sensitive to sub-nominal pilot inputs and maintenance-related issues.
This resource area contains descriptions of actual electronic systems failure
scenarios with an emphasis on the diversity of failure modes and effects that
can befall dependable systems.

Introductory pages begin [here](/dashlink/static/media/other/Introduction1.html).<br>
The descriptions begin
[here](/dashlink/static/media/other/ObservedFailures1.html).  These pages are
separated into sections.  Each section starts with a List of failure scenarios.  
In between the List slides are slides that give more information on those
scenarios which warrant more than a bullet or two of explanation.<br>
Some references are listed [here](/dashlink/static/media/other/References.html).<br>
A list of acronyms and initialisms is
[here](/dashlink/static/media/other/Acronyms_Initialisms.html).

If you would like to add a story to this list or add additional significant
details to an existing story, please contact Kevin Driscoll at
![](/dashlink/static/media/other/KevinDriscoll-email.PNG)

For a not-quite-working wiki subset of this Resource area, click on the Wiki link just to the left of this Summary or go to the URL [https://c3.nasa.gov/dashlink/projects/79/wiki/test_stories_split](/dashlink/projects/79/wiki/test_stories_split).

Also, those who log in can add comments to the Discussions at the bottom of this
page.
These grids were generated by Markus Ritter (DLR) using Solar in TAU format netcdf.nc.  The grids were converted to CGNS format by Pawel Chwalowski (NASA)
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following:

<pre>
-sensor recordings
-sampling rate
-units
-parameter description
-parameter ID
</pre>
Published at 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSM
17 - 19 September 2012, Indianapolis, Indiana
Matlab code to generate plots associated with the force fight associated a triple redundant command module under an inconsistent ommission error.
Modern space propulsion and exploration system designs are becoming increasingly
sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by
analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize
normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or
for analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle
system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring
System data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being
integrated to aid in fault detection will be described. The paper will also explain how Inductive Monitoring System can be used to complement a suite of other Integrated System Health Management tools, providing initial fault detection support for diagnosis and recovery.
When we address safety in a book on the business case for IVHM, the question arises whether safety isn’t inherently in conflict with the need of operators to run their systems as efficiently (and as cost effectively) as possible. The answer may be that the system needs to be just as safe as needed, but not significantly more. That begs the next question: How safe is safe enough? Several regulatory bodies provide guidelines for operational safety, but irrespective of that, operators do not want their systems to be known as lacking safety. We illuminate the role of safety within the context of IVHM.
This chapter discussed some of the algorithmic choices one encounters when designing an IVHM system. While it would be generally desirable to be able to pick a particular set of algorithms for a particular problem, the reality is a bit more complex. Depending on the budget, the performance requirements, the computational constraints, sensor availability, access to historical data, operational and environmental conditions, robustness to changing system configurations, algorithm maintenance needs, etc., no one algorithm will perform best in all situations. Indeed, it is necessary to evaluate these constraints during the algorithm design process and determine the best choice on a case-by-case analysis. The trade-offs between different choices are very real, and sometimes no solution can be found, which means that some of the constraints have to be relaxed. The simplest solution is generally preferred over a more complex one, but it is also important to consider that there is no free lunch. Finally, any health management solution also has to undergo verification and validation (V&V) and, in some cases, certification. Some of these issues are topics of other chapters in this book.
This chapter described at a very high level some of the considerations that need to be made when designing algorithms for a vehicle health management application. The choices made here affect the quality of the diagnosis and prognosis (covered in Chapter 7). Therefore, the algorithmic design choices are made in conjunction with the design choices for diagnostics and prognostics to optimally support these tasks. Furthermore, additional considerations imposed by computational constraints, resource availability, algorithm maintenance, need for algorithm re-tuning, etc. will impact the solutions.
It should also be noted that technological advances, both in hardware and software, impose the need for new solutions. For example, as new materials and new sensors are being developed, the algorithmic solutions will need to follow suit.
In general, there seems to be a trend to have more sensor data available. While this is potentially a good thing, sensor data provides value only when it is being processed and interpreted properly, in part by the techniques described here. Testing of the methods, however, requires the “right” kind of data. Generally, there is a lack of seeded fault data which are required to train and validate algorithms. It is also important to migrate information from the component to the subsystem to the system levels so that health management technologies can be applied effectively and efficiently at the vehicle level. It may be required to perform elements described in this chapter between different levels of the vehicle architecture.
This chapter presents several performance metrics for offline evaluation of prognostics algorithms. A brief overview of different methods employed for performance evaluation is also included. Because metrics developed in the context of forecasting differ from prognostics in the systems health management context and to account for the additional considerations, metrics specialized for prognostics (but not necessarily for the applica- tion) are needed. These metrics were introduced, and their use was illus- trated with recommendations.
Health monitoring is highly dependent on sensor systems that are capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system. Efforts are under way at NASA Glenn Research Center through support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such sensor technology for a wide variety of applications. These efforts are focused on developing high temperature, wireless, low cost, and durable products. In an effort to address technical issues concerning health monitoring, this article considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating turbine engine-like-disk to detect the disk faults and assess its structural integrity. The experimental results composed at a range of rotational speeds from tests conducted at the NASA Glenn Research Center’s Rotordynamics Laboratory are evaluated and integrated into multiple data-driven anomaly detection techniques to identify faults and anomalies in the disk. In summary, this study presents a select evaluation of online health monitoring of a rotating disk using high caliber capacitive sensors and demonstrates the capability of the in-house spin system.
With the advent and expansion of social networking, the amount of generated text
data has seen a sharp increase. In order to handle such a huge volume of text data, new and improved text mining techniques are a necessity. One of the characteristics of text data that makes text mining difficult, is multi-labelity. In order to build a robust and effective text classification method which is an integral part of text mining research, we must consider this property more closely. This kind of property is not unique to text data as it can be found in non-text (e.g., numeric) data as well. However, in text data, it is most prevalent. This property also puts the text classification problem in the domain of multi-label classification (MLC), where each instance
is associated with a subset of class-labels instead of a single class, as in conventional classification. In this paper, we explore how the generation of pseudo labels (i.e., combinations of existing class labels) can help us in performing better text classification and under what kind of circumstances. During the classification, the high and sparse dimensionality of text data has also been considered. Although, here we are proposing and evaluating a text classification technique, our main focus is on the handling of the multi-labelity of text data while utilizing the correlation among multiple labels existing in the data set. Our text classification technique is called pseudo-LSC (pseudo-Label Based Subspace Clustering). It is a subspace clustering algorithm that considers the high and sparse dimensionality as well as the correlation among different class labels during the classification process to provide better performance than existing approaches. Results on three real world multi-label data sets provide us insight into how the multi-labelity is handled in our classification process and shows the effectiveness of our approach.
In this paper we propose an innovative learning algorithm - a variation of One-class  Support Vector Machines (SVMs) learning algorithm to produce sparser solutions with much reduced computational complexities. The proposed technique returns an approximate solution, nearly as good as the solution set obtained by the classical approach, by minimizing the original risk function along with a regularization term. We introduce a bi-criterion optimization that helps guide the search towards the optimal set in much reduced
time. The outcome of the proposed learning technique was compared with the benchmark one-class Support Vector machines algorithm which more often leads to solutions with
redundant support vectors. Through out the analysis, the problem size for both optimization routines was kept consistent.
We have tested the proposed algorithm on a variety of data sources under different conditions to demonstrate the effectiveness. In all cases the proposed algorithm closely preserves the accuracy of standard one-class  SVMs while reducing both training time and test time by several factors.
This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and esti- mation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochem- ical processes in the form of equivalent electric circuit parame- ters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal frame- work. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.1

Index Terms—Battery health, Bayesian learning, particle filter, prognostics, relevance vector machine, remaining useful life.
Precursor parameters have been identified to enable development of a prognostic approach for insulated gate bipolar transistors (IGBT). The IGBT were subjected to thermal over- stress tests using a transistor test board until device latch-up. The collector-emitter current, transistor case temperature, transient and steady state gate voltages, and transient and steady state collector-emitter voltages were monitored in-situ during the test. Pre- and post-aging characterization tests were performed on the IGBT. The aged parts were observed to have shifts in capaci- tance-voltage (C-V) measurements as a result of trapped charge in the gate oxide. The collector-emitter ON voltage V ce(on) showed a reduction with aging. The reduction in the V ce(on) was found to be correlated to die attach degradation, as observed by scan- ning acoustic microscopy (SAM) analysis. The collector-emitter voltage, and transistor turn-off time were observed to be precursor parameters to latch-up. The monitoring of these precursor param- eters will enable the development of a prognostic methodology for IGBT failure. The prognostic methodology will involve trending precursor data, and using physics of failure models for prediction of the remaining useful life of these devices.
We were interested here in particular in conditions where un-modeled effects are present as manifested by the different degradation curve at 45°C. Although all algorithms were given the same amount of information to the degree practical, there were considerable differences in performance. Specifically, the combined Bayesian regression-estimation approach implemented as a RVM-PF framework has significant advantages over conventional methods of RUL estimation like ARIMA and EKF. ARIMA, being a purely data-driven method, does not incorporate any physics of the process into the computation, and hence ends up with wide uncertainty margins that make it unsuitable for long-term predictions. Additionally, it may not be possible to eliminate all non-stationarity from a dataset even after repeated differencing, thus adding to prediction inaccuracy. EKF, though robust against non-stationarity, suffers from the inability to accommodate un-modeled effects and can diverge quickly as shown. We did not explore other variations of the Kalman Filter that might provide better performance such as the unscented Kalman Filter. The Bayesian statistical approach, on the other hand, appears to be well suited to handle various sources of uncertainties since it defines probability distributions over both parameters and variables and integrates out the nuisance terms. Also, it does not simply provide a mean estimate of the time-to-failure; rather it generates a probability distribution over time that best encapsulates the uncertainties inherent in the system model and measurements and in the core concept of failure prediction.
A general framework for probabilistic prognosis using maximum entropy approach, MRE, is proposed in this paper to include all available information and uncertainties for RUL prediction. Prognosis metrics are used for model comparison and performance evaluation. Several conclusions can be drawn based on the results in the current investigation: The proposed MRE updating approach results in more accurate and precise prediction compared with the classical Bayesian method. The classical Bayesian method is a special case of the proposed MRE approach and MRE approach is more flexible to include additional information for inference, which cannot be handled by the classical Bayesian method. The prognosis metrics can be successfully used for algorithm comparison and can give quantitative values in model (algorithm) performance evaluation. A robustness metric measuring the updating algorithmic sensitivity to prior uncertainty is proposed and applied to both Bayesian and MRE updating approaches. The application examples show that MRE exhibits more robustness against the uncertainty introduced by parameter distribution priors in the sense of prognosis performance. It is important to realize when to apply these metrics to arrive at meaningful interpretations. For instance, use of the convergence metric makes sense only when the algorithm predictions converge (get better) with time.
This paper presents a case study where a RSPF-based prognosis framework is applied to estimate the remaining useful life of an energy storage device (Li-Ion battery). A comparison based on prognosis performance metrics indicates that the RSPF-based prognostic approach is more suitable than classic PF methods to represent rare events such as capacity regeneration phenomena between charging periods, in terms of accuracy of the state estimate and steadiness of the RUL estimate. We surmise that the existence of particles in the tails of the state pdf allow the RSPF-based prognostic algorithm to generally provide a more conservative estimate of the RUL of the faulty piece of equipment. We surmise that it also helps to incorporate the probability of rare and costly events in the evolution of the fault condition in time.
Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reli- able than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.
A method has been developed for prognostication of accrued prior damage in electronics subjected to overlapping sequential environments of thermal aging and thermal cycling. The presented approach uses the LM Algorithm in conjunction with microstructural evolution of damage based leading indi- cator for estimating prior stress history. Specific damage proxies examined include the phase-growth indicator and the IMC thickness. The viability of the approach has been demonstrated for leadfree test assemblies subjected to thermal aging at 125 °C and redeployed in cyclic thermo-mechanical environment −55–125 °C. Damage equivalency relationships between thermal aging during storage life and the resulting reduction in thermo-mechanical reliability in cyclic thermal environments during field deployment has been derived and validated based on two damage proxies. Convergence of the damage mapping to a common solution from data based on the two separate leading indicators has been demonstrated. Assemblies subjected to sequential stresses have been prog- nosticated for accrued damage from sequential overlapping stresses of thermal aging and thermal cycling. Correlation between the prognosticated damage and the actual accrued damage demonstrates that the proposed approach can be used to assess damage accrued under overlapping thermo- mechanical stresses of thermal aging and thermal cycling. In addition, prognostics metrics have been used to quantitatively evaluate the performance of the prognostic algorithms using both the leading indicators. Results demonstrate that both damage proxies work well in estimating accrued damage and estimating residual life.
This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adap- tation. A metric representing the relative deviation between the nominal output of a system and the net output that is ac- tually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formu- lated Markov process. The state space of the Markov pro- cess will be defined in terms of an abstracted metric repre- senting the relative health remaining in each of the system’s components. The proposed formulation of component fault dynamics will conveniently relate feasible system output per- formance modifications to predictions of future component health deterioration.
Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the sys- tem. Prognostics algorithms must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajecto- ries of a system by using the unscented transform. Using this approach, we are not only able to reduce the computa- tional load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experi- mental data from a battery testbed.
Electrolytic capacitors are used in several applications rang- ing from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuators. Past expe- riences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress condi- tions during operations. This makes them good candidates for prognostics and health management. Model-based prognos- tics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of health and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation mod- els for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.
An integrated fatigue damage diagnosis and prognosis framework is proposed in this paper. The proposed methodology integrates a Lamb wave-based damage detection technique and a Bayesian updating method for remaining useful life (RUL) prediction. First, a piezoelectric sensor network is used to detect the fatigue crack size near the rivet holes in fuselage lap joints. Advanced signal processing and feature fusion is then used to quantitatively estimate the crack size. Following this, a small time scale model is introduced and used as the mechanism model to predict the crack propagation for a given future loading and an estimate of initial crack length. Next, a Bayesian updating algorithm is implemented incorporating the damage diagnostic result for the fatigue crack growth prediction. Probability distributions of model parameters and final RUL are updated considering various uncertainties in the damage prognosis process. Finally, the proposed methodology is demonstrated using data from fatigue testing of realistic fuselage lap joints and the model predictions are validated using prognostics metrics.
An overview of the current results of prognostics for DC- DC power converters is presented, focusing on the output filter capacitor component. The electrolytic capacitor used typically as fileter capacitor is one of the components of the power supply with higher failure rate, hence the effort in devel- oping component level prognostics methods for capacitors. An overview of prognostics algorithms based on electrical overstress and thermal overstress accelerated aging data is presented and a discussion on the current efforts in terms of validation of the algorithms is included. The focus of current and future work is to develop a methodology that allows for algoritm development using accelerated aging data and then transform that to a valid algorithm on the real usage time scale.
This paper discusses experimental setups for health monitoring and prognostics of electrolytic capacitors under nominal operation and accelerated aging conditions. Electrolytic capacitors have higher failure rates than other components in electronic systems like power drives, power converters etc. Our current work focuses on developing first-principles-based degradation models for electrolytic capacitors under varying electrical and thermal stress conditions. Prognostics and health management for electronic systems aims to predict the onset of faults, study causes for system degradation, and accurately compute remaining useful life. Accelerated life test methods are often used in prognostics research as a way to model multiple causes and assess the effects of the degradation process through time. It also allows for the identification and study of different failure mechanisms and their relationships under different operating conditions. Experiments are designed for aging of the capacitors such that the degradation pattern induced by the aging can be monitored and analyzed. Experimental setups and data collection methods are presented to demonstrate this approach.
Prior research into metrics and design for autonomy were presented. At this time, the prospect of adding significant autonomous decision-making on a piloted aircraft is viewed with some degree of concern for the ability of the system to add value without adding risk.
A contrived example of a dice throwing game was considered in order to provide some insight into the general problem developing prognostics-based control routines that utilize uncertain models of component fault dynamics and future environmental stresses to assess and mitigate risk. A generalized Markov modeling representation of fault dynamics was developed for the case that available modeling of fault growth physics and available modeling of future environ- mental stresses are represented by two independent Markov process models. A finite horizon dynamic programming algorithm was given for a Markov decision process representation of the prognostics-based control problem and this algorithm was used to identify an optimal control policy for the dice throwing game that is considered in this paper. The outcomes obtained from simulations of the optimizing control policy were observed to differ only slightly from the outcomes that would have been achievable if all modeling uncertainties were removed from the example dice throwing game.
This article presented a discussion on uncertainty representation and management for model-based prog- nostics methodologies based on the Bayesian tracking framework and specifically for a Kalman filter appli- cation to electronics components. In particular, it explores the implication of modeling remaining useful life prediction as a stochastic process and how it relates to remaining useful life computation by statistical models, to uncertainty representation and management, and to the role of prognostics in decision-making. A discussion on how uncertainty propagates from the health state estimation process through the health state forecasting process is provided. Remaining useful life computation steps under uncertainty are pre- sented and analytical results on uncertainty quantification are provided under a simplified scenario. A proper propagation of uncertainty through the RUL prediction step as well as its correct interpretation are key to developing decision-making methodologies that make use of the remaining useful life prediction estimates and their corresponding uncertainties in order to make actionable choices that will optimize reliability, operations or safety in view of the prognostics information.
This paper proposes first principles based modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors and MOSFETs are the two major components, which cause degradations and failures in DC-DC converters. This type of capacitors are known for its low reliability and frequent breakdown on critical systems like power supplies of avionics equipment and electrical drivers of electro-mechanical actuators. Some of the more prevalent fault effects, such as a ripple voltage surge at the power supply output can cause glitches in the GPS position and velocity output, and this, in turn, if not corrected will propagate and distort the navigation solution. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. In this paper, we study the effects of accelerated aging due to thermal stress on sets of capacitors. Our focus is on deriving rst principles degradation models for thermal stress conditions. The degradation data form the basis for developing the model based remaining life prediction algorithm. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.
Abstract — A possible resident of smart home is an old person or an Alzheimer patient that should be assisted continuously for the rest of his life; however, normally this person desires to live independently at home. Typically, this person may forget sometimes completion of the activities; may realize the activities of daily living incorrectly, and may enter to dangerous states. In this context smart home project is proposed as an ambient intelligent environment, in which on one hand the resident is observed continuously through the embedded sensors, and on the other hand the resident is assisted automatically through the embedded electronically controllable actuators. In this work, we propose an approach to interpret the sensors’ observations and how to automatically reason in the required assistance. The result is provision of automated assistance for the smart home resident.
Rover missions typically involve visiting a set of predetermined waypoints to perform science functions, such as sample collection. Given the communication delay between Earth and the rover, and the possible occurrence of faults, an autonomous decision making system is essential to ensure that the rover maximizes the scientific operations performed without damaging itself further or stalling. This paper presents a modular software architecture for autonomous decision making for rover operations that uses diagnostic and prognostic information to influence mission planning and decision making to maximize the completion of mission objectives. The decision making system consists of separate modules that perform the functions of control, diagnosis, prognosis, and decision making.We demonstrate our implementation of this architecture on a simulated rover testbed.
In summary, this paper lays a simple flight plan optimization strategy based on the particle filtering framework described in [5]. This is meant as a first step in formalizing computationally tractable stochastic programming techniques to optimally generate flight plans in response to battery life predictions. This approach takes advantage of the PF framework to simultaneously generate the optimal/sub-optimal flight plan simultaneously with predicting the RUL. Several steps lie ahead like a comparative analysis of alternative stochastic models in terms of optimality as well as computational cost. These options will need to be validated by flight tests where robustness to environmental conditions like air temperature and density as well as wind speed can be evaluated. The notion of risk-tolerance can be introduced via appropriate objective functions, thus allowing a non-zero risk of the dead stick condition in order to use more battery power.
We report here on first steps towards integrating systems health monitoring with adaptive contingency controls. In the scenario considered, the adaptive controller receives specific information about damage to a blade and reacts to turbulent conditions where the blade might incur additional damage. The controller uses an observer to determine when the generator set point should be lowered, thereby causing the blades to be pitched at a lower wind speed. The loads on the blades are reduced when the blades are set at larger pitch angles. This is meant to decrease the damage propagation. The adaptive contingency controller can be tuned to find the optimal trade-off between maximized damage suppression and maximized power generation. Future work will address the assessment of blade health in the presence of turbulent wind conditions.
We detail all of the facets of adapting classical model checking to a real aerospace system, in- cluding deriving the formal model and a set of specifications from natural language descriptions. To ensure the model checking results are meaningful, we have to ensure that both the model and specifications correctly reflect the intentions of the designers, thus we employ model validation and property debugging techniques. We demonstrate the utility of enhancing LTL satisfiability checking by taking the fairness constraints of the system model into consideration. We argue that specification debugging in real applications deserves more attention in future research efforts, and the utility of a system formalization, model and specification debugging, and verification trilogy for model checking real systems under development.
In this paper we assume there are no hardware failures or lost messages. As the AAC design develops and hardware details are decided by AAC designers, we plan to take the failure rates of the chosen components into consideration, i.e. by extending our work to probabilistic model checking using PRISM [19]. Previous work has reported on analyzing the safety of air traffic control systems using simulation [3] or fault trees [1]. By extending the model we designed in this paper, we can carry out safety analysis using PRISM to capture the dynamic interactions in the AAC.
The translation of temporal logic specifications constitutes an essen- tial step in model checking and a major influence on the efficiency of formal verification via model checking. We devise a new explicit-state translation of Lin- ear Temporal Logic to automata for the class of LTL specifications that describe safety properties, arguably the most used formal specifications in real-world sys- tems. By exploiting the inherent determinism in safety specifications, we can build deterministic Promela never claims that accept only the bad prefixes of the safety specification. In contrast to previous works, we focus on compilation to never claims rather than simply automata and measure Spin model-checking time separately from compilation time and automata size. An extensive experi- mental evaluation over a space of configurations demonstrates that our new trans- lation consistently results in better model-checking performance, for a large array of benchmarks, over the best current translation.
The ability to utilize prognostic system health information in operational decision making, especially when fused with information about future operational, environmental, and mission requirements, is becoming desirable for both manned and unmanned aerospace vehicles. A vehicle capable of evaluating its own health state and making (or assisting the crew in making) decisions with respect to its system health evolution over time will be able to go further and accomplish more mission objectives than a vehicle fully dependent on human control. This paper describes the development of a hardware testbed for integration and testing of prognostics-enabled decision making technologies. Although the testbed is based on a planetary rover platform (K11), the algorithms being developed on it are expected to be applicable to a variety of aerospace vehicle types, from unmanned aerial vehicles and deep space probes to manned aircraft and spacecraft. A variety of injectable fault modes is being investigated for electrical, mechanical, and power subsystems of the testbed. A software simulator of the K11 has been developed, for both nominal and off-nominal operating modes, which allows prototyping and validation of algorithms prior to their deployment on hardware. The simulator can also aid in the decision-making process. The testbed is designed to have interfaces that allow reasoning software to be integrated and tested quickly, making it possible to evaluate and compare algorithms of various types and from different sources. Currently, algorithms developed (or being developed) at NASA Ames - a diagnostic system, a prognostic system, a decision-making module, a planner, and an executive - are being used to complete the software architecture and validate design of the testbed.
An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die- attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
It is an inescapable truth that no matter how well a system is designed it will degrade, and if degrading parts are not repaired or replaced the system will fail. Avoiding the expense and safety risks associated with system failures is certainly a top priority in many systems; however, there is also a strong motivation not to be overly cautious in the design and maintenance of systems, due to the expense of maintenance and the undesirable sacrifices in performance and cost effectiveness incurred when systems are over designed for safety. This paper describes an analytical process that starts with the derivation of an expression to evaluate the desirability of future control outcomes, and eventually produces control routines that use uncertain prognostic information to optimize derived risk metrics. A case study on the design of fault-adaptive control for a skid-steered robot will illustrate some of the fundamental challenges of prognostics-based control design.
Power electronics play an increasingly important role in energy applications as part of their power converter circuits. Understanding the behavior of these devices, especially their failure modes as they age with nominal usage or sudden fault development is critical in ensuring efficiency. In this paper, a prognostics based health management of power MOSFETs undergoing accelerated aging through electrical overstress at the gate area is presented. Details of the accelerated aging methodology, modeling of the degradation process of the device and prognostics algorithm for prediction of the future state of health of the device are presented. Experiments with multiple devices demonstrate the performance of the model and the prognostics algorithm as well as the scope of application.
Index Terms—Power MOSFET, accelerated aging, prognostics.
This paper presents a novel battery health management technology for the new generation of electric unmanned aerial vehicles powered by long-life, high-density, scalable power sources. Current reliability based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The technique presented here encodes the basic electrochemical processes of a Lithium-polymer battery in an advanced Bayesian inference framework to simultaneously track battery state-of-charge as well as tune the battery model to make accurate predictions of remaining useful life. Results from ground tests with emulated flight profiles are presented with discussions on the use of such prognostics results for decision making.
In summary, this paper lays out a novel battery health management technique for application onboard an electric UAV. This technique is also applicable to other electric vehicles, like electric cars. Electric cars are not new; they were more popular and reliable at the turn of the twentieth century as compared to their gasoline powered rivals. However, the uncertainty in determining battery life plagued electric vehicles then as it does now. A recent report by the Consumer Electronics Association, ―Electric Vehicles: The Future of Driving‖, indicates that although these vehicles are increasing in popularity, running out of battery power on the road is the top concern for consumers (71% of adults surveyed). Thus developing advanced battery health management techniques is very important. the RUL pdf. This is turn can motivate and inform mission replanning activity. The PF prognostic framework can also be used to evaluate the effectiveness of possible mitigation actions, thus improving operational safety and optimizing battery power.
The sample scenarios provided here are competition scenarios from previous DXC competitions. They are identical to the competition data associated with previous years' projects, but also listed here for convenience. The zip files have a spreadsheet that lists the scenarios and relevant fault information. Each scenario is provided in three formats: tab-delimited .txt file, Matlab .mat file, and tab-delimited .scn file. The different formats are provided for your convenience; they have the same data. The scenario (.scn) files are the format read by the DXC framework. See the system catalogs and schematics for additional information.
This is the DXC Framework for Windows Operating Systems
DXC Framework for Linux and Unix type operating systems
The objective of this work includes the coastal scenario, risks and development of coastal paleoclimatology through landscape mapping; by highlighting the devastating climate change impacts that might result in tsunami in South-East Asian sea basin with short or no-awareness period; despite the facts that the Southeast Asia region is generally poor being encompassed by twelve countries along with the Indian and Pacific Oceans. Special payable concern to the Bay of Bengal has been paid that can dictate region’s climate to certain extent. The ecosystems’ impact due to climate change and global warming -can bring direct variables and affects in –salinity, temperature, river flow, runoff, soil characteristics, erosion, nutrition level and water quality. The landscape mapping can address the system infrastructure requirements to the SEAR’s sea basin attainable by the annual monsoons, the Southwest and the Northeast Monsoons. How recent meteorological and geodynamic-genetic events can result in adverse economical damages and significant losses of lives are also drawn. This work monitors on Sea-level rise gets projected under global warming. The most brainstorming findings from climate change issues are how the high latitudes for SEAR Sea Basins’ are likely to experience greater warming than the global mean and warming,- And how the hydrological cycle gets found responsible for bringing more floods and more droughts in- causing huge devastating changes for environmental factors in coastal zones
Most fault adaptive control research addresses the preservation of system stability or functionality in the presence of a specific failure (fault). This paper examines the fault adaptive control problem for a generic class of incipient failure modes, which do not initially affect system stability, but will eventually cause a catastrophic failure to occur. This risk of catastrophic failure due a component fault mode is some monotonically increasing function of the load on the component. Assuming that a probabilistic prognostic model is available to evaluate the risk of incipient fault modes growing into catastrophic failure conditions, then fundamentally the fault adaptive control problem is to adjust component
loads to minimize risk of failure, while not overly degrading nominal performance. A methodology is proposed for posing this problem as a finite horizon constrained optimization, where constraints correspond to maximum risk of failure and maximum deviation from nominal performance. Development of the methodology to handle a general class of overactuated systems is given. Also, the fault adaptive control methodology is demonstrated on an application example of practical significance, an electro-mechanical actuator (EMA) consisting of three DC motors geared to the same output shaft. Similar actuator systems are commonly used in aerospace, transportation, and industrial processes to actuate critical loads, such as aircraft control surfaces. The fault mode simulated in the system is a temperature dependent motor winding insulation degradation.
Understanding the ageing mechanisms of electronic components critical avionics systems such as the GPS and INAV are of critical importance. Electrolytic capacitors and MOSFET’s have higher failure rates among the components of DC-DC power converter systems. Our current work focuses on analyzing and modeling electrolytic capacitor degradation and its effects on the output of DC-DC converter systems. The output degrada- tion is typically measured by an increase in ESR (Equivalent Series Resistance) and decrease in the capacitance value over long periods of use even under nominal operating conditions. Typically the primary effect of degradation is increased ripple current and this has adverse effects on downstream components. For example, in avionics systems where the power supply drives a GPS unit, ripple currents can cause glitches in the GPS position and velocity output, and this may cause errors in the Inertial Navigation (INAV) system, causing the aircraft to fly off course. In this paper, we present the details of our ageing methodology along with details of experiments and analysis of the results.
This paper presents research results dealing with power MOSFETs (metal oxide semiconductor field effect tran- sistor) within the prognostics and health management of electronics. Experimental results are presented for the identification of the on-resistance as a precursor to failure of devices with die-attach degradation as a failure mechanism. Devices are aged under power cycling in order to trigger die-attach damage. In situ measurements of key electrical and thermal parameters are collected throughout the aging process and further used for analysis and computation of the on-resistance parameter. Experimental results show that the devices experience die-attach damage and that the on-resistance captures the degradation process in such a way that it could be used for the development of prognostics algorithms (data-driven or physics-based).
Prognostics is an engineering discipline that focuses on estimation of the health state of a component and the prediction of its remaining useful life (RUL) before failure. Health state estimation is based on actual conditions and it is fundamental for the prediction of RUL under anticipated future usage. Failure of electronic devices is of great concern as future aircraft will see an increase of electronics to drive and control safety-critical equipment throughout the aircraft. Therefore, development of prognostics solutions for electronics is of key importance. This paper presents an accelerated aging system for gate-controlled power transistors. This system allows for the understanding of the effects of failure mechanisms, and the identification of leading indicators of failure which are essential in the development of physics-based degradation models and RUL prediction. In particular, this system isolates electrical overstress from thermal overstress. Also, this system allows for a precise control of internal temperatures, enabling the exploration of intrinsic failure mechanisms not related to the device packaging. By controlling the temperature within safe operation levels of the device, accelerated aging is induced by electrical overstress only, avoiding the generation of thermal cycles. The temperature is controlled by active thermal-electric units. Several electrical and thermal signals are measured in-situ and recorded for further analysis in the identification of leading indicators of failures. This system, therefore, provides a unique capability in the exploration of different failure mechanisms and the identification of precursors of failure that can be used to provide a health management solution for electronic devices.
This paper proposes the experiments and setups for studying diagnosis and prognosis of electrolytic capacitors in DC-DC power converters. Electrolytic capacitors and power MOSFET’s have higher failure rates than other components in DC-DC converter systems. Currently, our work focuses on experimental analysis and modeling electrolytic capacitors degradation and its effects on the output of DC-DC converter systems. The output degradation is typically measured by the increase in Equivalent series resistance and decrease in capacitance leading to output ripple currents.Typically, the ripple current effects dominate, and they can have adverse effects on downstream components.
A model based approach to studying degradation phenomena enables us to combine the physics based modeling of the DC-DC converter with physics of failure models of capacitor degradation, and predict using stochastic simulation methods how system performance deteriorates with time. Degradation experiments were conducted where electrolytic capacitors were subjected to electrical and thermal stress to accelerate the aging of the system. This more systematic analysis may provide a more general and accurate method for computing the remaining useful life (RUL) of the component and the converter system.
Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper, we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.
This paper proposes a combined energy-based model with an empirical physics of failure model for degradation analysis and prognosis of electrolytic capacitors in DC-DC power converters. Electrolytic capacitors and MOSFET’s have higher failure rates than other components in DC-DC converter systems. For example, in avionics systems where the power supply drives a GPS unit, ripple currents can cause glitches in the GPS position and velocity output, and this may cause errors in the Inertial Navigation (INAV) system causing the aircraft to fly off course. We have employed a topological energy based modeling scheme based on the bond graph (BG) modeling language for building parametric models of electrical domain systems. Our current work adopts a physics of failure model (Arrhenius Law) for equivalent series resistance (ESR) increase in electrolytic capacitors subjected to electrical and thermal stresses. Experiments for capacitor degradation were conducted for collecting degradation ESR data. Parameter re-estimation for the failure model is done using the experimental data. The derived degradation model of the capacitor is reintroduced into the DC-DC converter system model to study changes in the system performance using Monte Carlo simulation methods. Stochastic simulation methods applied to the combined model help us predict how system performance deteriorates with time.
Particle filters (PF) have been established as the de facto state of the art in failure prognosis, and particularly in the representation and management of uncertainty in long-term predictions when used in combination with outer feedback correction loops. This paper presents a novel Risk- Sensitive PF (RSPF) framework that complements the benefits of the classic approach, by representing the probability of rare and costly events within the formulation of the nonlinear dynamic equation that describes the evolution of the fault condition in time. The performance of this approach is thoroughly compared using a set of proposed metrics for prognosis results. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.
Actuator systems are employed widely in aerospace, transportation and industrial processes to provide power to critical loads, such as aircraft control surfaces. They must operate reliably and accurately in order for the vehicle / process to complete successfully its designated mission. Incipient actuator failure conditions may severely endanger the operational integrity of the vehicle / process and compromise its mission. The ability to maintain a stable and credible operation, even in the presence of incipient failures, is of paramount importance to accomplish “must achieve” mission objectives. This paper introduces a novel methodology for the fault-tolerant design of critical subsystems, such as an ElectroMechanical Actuator (EMA), that takes advantage of on-line, real-time estimates of the Remaining Useful Life (RUL) or Time-to-Failure (TTF) of a failing component and reconfigures the available control authority by trading off system performance with control activity. The primary goal is to complete critical mission objectives within a time window dictated by prognostic algorithms so that the fault mode is accommodated and an acceptable level of performance maintained for the duration of the mission. The proposed fault-tolerant control design is mathematically rigorous, generic and applicable to a variety of application domains. An EMA is used to illustrate the efficacy of the proposed approach.
Distributed wireless architectures for prognostics is an important enabling step in prognostic research in order to achieve feasible real-time system health management. A significant problem encountered in implementation of such architectures is power management. In this paper, we present robust power management techniques for a generic health management architecture that involves diagnostics and prognostics for a system comprising multiple heterogeneous components. Our power management techniques are based on online dynamic monitoring of the sensor battery discharge profile which enables accurate predictions of when the device should be put into low power modes. In our architecture, low power mode is achieved by run-time sampling rate modification through sleep states. Our experiments with a cluster of smart sensors for a hybrid diagnostics and prognostics architecture show significant gains in power management without severe loss in performance.
This paper presents an empirical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.*
Prognostics performance evaluation has gained significant attention in the past few years. *As prognostics technology matures and more sophisticated methods for prognostic uncertainty management are developed, a standardized methodology for performance evaluation becomes extremely important to guide improvement efforts in a constructive manner. This paper is in continuation of previous efforts where several new evaluation metrics tailored for prognostics were introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. Several shortcomings identified, while applying these metrics to a variety of real applications, are also summarized along with discussions that attempt to alleviate these problems. Further, these metrics have been enhanced to include the capability of incorporating probability distribution information from prognostic algorithms as opposed to evaluation based on point estimates only. Several methods have been suggested and guidelines have been provided to help choose one method over another based on probability distribution characteristics.
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison. It is shown that the proposed maximum relative entropy methodology has narrower confidence bounds of the remaining life prediction than classical Bayesian updating algorithm.
Prognostics and health management (PHM) is a maturing system engineering discipline. As with most maturing disciplines, PHM does not yet have a universally accepted research methodology. As a result, most component life estimation efforts are based on ad-hoc experimental methods that lack statistical rigor. In this paper, we provide a critical review of current research methods in PHM and contrast these methods with standard research approaches in a more established discipline (medicine). We summarize the developmental steps required for PHM to reach full maturity and to generate actionable results with true business impact.
Distributed prognostics is the next step in the evolution of prognostic methodologies. It is an important enabling technology for the emerging Condition Based Management/Prognostics Health Management paradigm. This paper provides an overview of such systems with details of the system architectures and the various possible design considerations. A distributed particle filter architecture for battery health management is developed and successfully implemented using embedded smart sensor devices with wireless communication capabilities.
Particle filters (PF) have been established as the de facto state of the art
in failure prognosis. They combine advantages of the rigors of Bayesian estimation
to nonlinear prediction while also providing uncertainty estimates with a given solution. Within the context of particle filters, this paper introduces several novel methods for uncertainty representations and uncertainty management. The prediction uncertainty is modeled via a rescaled Epanechnikov kernel and is assisted with resampling techniques and regularization algorithms. Uncertainty management is accomplished through parametric adjustments in a feedback correction loop of the state model and its noise distributions. The correction loop provides the mechanism to incorporate information that can improve solution accuracy and reduce uncertainty bounds. In addition, this approach results in reduction in computational burden. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.
Prognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenancehave been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.
This paper describes how damage propagation can be modeled within the modules of aircraft gas turbine engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine as a function of variations of flow and efficiency of the modules of interest. An exponential rate of change for flow and efficiency loss was imposed for each data set, starting at a randomly chosen initial deterioration set point. The rate of change of the flow and efficiency denotes an otherwise unspecified fault with increasingly worsening effect. The rates of change of the faults were constrained to an upper threshold but were otherwise chosen randomly. Damage propagation was allowed to continue until a failure criterion was reached. A health index was defined as the minimum of several superimposed operational margins at any given time instant and the failure criterion is reached when health index reaches zero. Output of the model was the time series (cycles) of sensed measurements typically available from aircraft gas turbine engines. The data generated were used as challenge data for the Prognostics and Health Management (PHM) data competition at PHM’08.
To advance the field of electronics prognostics, the study of transistor fault modes and their precursors is essential. This paper reports on a platform for the aging, characterization, and scenario simulation of gate controlled power transistors. The platform supports thermal cycling, dielectric over-voltage, acute/chronic thermal stress, current overstress and application specific scenario simulation. In addition, the platform supports in- situ transistor state monitoring, including measurements of the steady-state voltages and currents, measurements of electrical transient response, measurement of thermal transients, and extrapolated semiconductor impedances, all conducted at varying gate and drain voltage levels. The aging and characterization platform consists of an acquisition and aging hardware system, an agile software architecture for experiment control and a collection of industry developed test equipment.
In situations where the cost/benefit analysis of using physics-based damage propagation algorithms is not favorable and when sufficient test data are available that map out the damage space, one can employ data-driven approaches. In this investigation, we evaluate different algorithms for their suitability in those circumstances. We are interested in assessing the trade-off that arises from the ability to support uncertainty management, and the accuracy of the predictions. We compare here a Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), and a Neural Network-based approach and employ them on relatively sparse training sets with very high noise content. Results show that while all methods can provide remaining life estimates although different damage estimates of the data (diagnostic output) changes the outcome considerably. In addition, we found that there is a need for performance metrics that provide a comprehensive and objective assessment of prognostics algorithm performance.
Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.1 2
Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.
The term Automated Contingency Management (ACM) has been used to describe intelligent systems capable of mission re-planning and control reconfiguration in the presence of a current health state diagnosis. While a diagnostics driven ACM capability designed to optimize multi-objective performance criteria remains a significant technical challenge, it cannot hope to overcome the fact that it will always be a reactive paradigm. This paper, therefore, introduces an automated contingency management paradigm based on both current heath state (diagnosis) and future health state estimates (prognosis). Including Prognostics in the control loop poses at least two additional challenges to ACM. First, future state prediction will, in general, have uncertainty that increases as the prediction horizon increases so adaptive prognosis routines that manage uncertainty are critical. Secondly, a warning period afforded by prognosis allows ACM to be split into a real- time “reactive” component and a non-real time “planning” component that considers temporal parameters and the potential impact of being proactive with mitigating action. The proactive ACM paradigm was developed and evaluated in the context of a generic mono-propellant system model in Simulink/Stateflow with diagnostics, prognostics and an optimal reconfigurable control system. Applications of Artificial Intelligence (AI) technologies in prognostics enhanced ACM system are briefly discussed. Preliminary results from the on-going research work are presented and the paper is concluded with remarks on future work.
A prognostic system makes it possible to anticipate loss of functionality before it occurs with sufficient lead time to take actions that mitigate the impact of this loss. We focus on the forms of mitigation within the flight vehicle that influence the operational dynamics but do not directly amend the mission plan. Thus, we focus upon the reconfiguration of the feedback control strategy for the flight system.

The high degree of complexity in the design and dynamics of modern aircraft is typically handled using a hierarchical control scheme in which there are several levels of control at increasing levels of responsibility: the component level, the subsystem level, and the system level. Our reconfiguration strategy involves mitigating problems that are detected at the component level at both the level in which the fault is detected and higher levels as well. There are, thus, two subproblems to the reconfiguration: (a) an adaptive control problem at the lower level to extend component life and derive new component performance limits, and (b) a supervisory control problem at the higher level to adapt the system controller to maximize system capability while respecting the performance limitations. Since our reconfiguration occurs in the context of a dynamic system, we need to respect the stability implications of the reconfiguration. To address this, we apply bandwidth analyses at the component level and the systems level in a robust performance context. A conservative criterion for stability is to impose rate limits for reconfiguration that insure that undesired, and possibly unmodeled, modes of behavior are not driven by reconfiguration activities. For specific hardware, extensions beyond this conservative approach may be warranted (e.g. to catch faulty behavior) and validated on a case-by-case basis, essentially by extending the component modeling to include a model of behavior under certain types of reconfiguration.
The estimation of remaining useful life (RUL) of a faulty component is at the center of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. This is especially true for aerospace systems, where unanticipated subsystem downtime may lead to catastrophic failures. RUL prediction needs to contend with multiple sources of error like modeling inconsistencies, system noise and degraded sensor fidelity. Bayesian theory of uncertainty management provides a way to contain these problems by integrating out the nuisance variables. We use the Relevance Vector Machine (RVM), for model development. RVM is a Bayesian treatment of the well known Support Vector Machine (SVM), a kernel-based regression/classification technique. This model is next used in a Particle Filter (PF) framework. Statistical estimates of the noise in the system and anticipated operational conditions are processed to provide estimates of RUL in the form of a probability density function (PDF). Validation of this approach on experimental data collected from Li-ion batteries is presented.
The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation via Particle Filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of- life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures.

Batteries, composed of multiple electro- chemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.
This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.
The autonomous systems (AS) project, led by NASA Ames, is developing software for system operation automation. AS technology will help astronauts make more decisions without the assistance of people on the ground, providing software for automatic diagnosis of failures in a spacecraft of other system, and software to automate the execution of sequences of actions at the discretion of human operators. In June, AS software increased coordination capability while decreasing workload under varying operational scenarios, time delays, and levels of crew autonomy during the autonomous mission operations experiment in the Deep Space Habitat at Johnson.
This Amphibious UAV, less than 10 meters in length is a perfect scout for Naval Reconnaissance. It can be greatly effective for detecting incoming enemy subs or naval crafts.
Model-based prognostics approaches rely on physics-based models that describe the behavior of systems and their components. These models must account for the several different damage processes occurring simultaneously within a component. Each of these damage and wear processes contribute to the overall component degradation. We develop a model-based prognostics methodology that consists of a joint state-parameter estimation problem, in which the state of a system along with parameters describing the damage progression are estimated, followed by a prediction problem, in which the joint state-parameter estimate is propagated forward in time to predict end of life and remaining useful life. The state-parameter estimate is computed using a particle filter, and is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control algorithm that maintains an uncertainty bound around the unknown parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump that includes damage progression models, to which we apply our model-based prognostics algorithm. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the approach when multiple damage mechanisms are active.
Requirements for system availability for ultra-high reliability electronic systems such as airborne and space electronic systems are driving the need for advanced heath monitoring techniques for early detection of the onset of damage. Aerospace-electronic systems usually face a very harsh environment, requiring them to survive the high strain rates, e.g. during launch and re-entry and thermal environments including extreme low and high temperatures. Traditional health monitoring methodologies have relied on reactive methods of failure detection often providing little or no insight into the remaining useful life of the system. In this paper, a mathematical approach for interrogation of system state under cyclic thermo- mechanical stresses has been developed for 6-different leadfree solder alloy systems. Data has been collected for leading indicators of failure for alloy systems including, Sn3Ag0.5Cu, Sn3Ag0.7Cu, Sn1Ag0.5Cu, Sn0.3Ag0.5Cu0.1Bi, Sn0.2Ag0.5Cu0.1Bi0.1Ni, 96.5Sn3.5Ag second-level interconnects under the application of cyclic thermo-mechanical loads. Methodology presented resides in the pre-failure space of the system in which no macro-indicators such as cracks or delamination exist. Systems subjected to thermo-mechanical damage have been interrogated for system state and the computed damage state correlated with known imposed damage. The approach involves the use of condition monitoring devices which can be interrogated for damage proxies at finite time- intervals. Interrogation techniques are based on derivation of damage proxies, and system prior damage based non-linear least- squares methods including the Levenberg-Marquardt Algorithm. The system’s residual life is computed based on residual-life computation algorithms.
One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well- designed prognostic model that can take advantage of the model adaptation properties of a particle filter.*
Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the un- derlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model- based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refuel- ing system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evalu- ate its effectiveness and robustness. The approach is demon- strated using historical pneumatic valve data from the refuel- ing system.
Structural damage to ball grid array interconnects incurred during vibration testing has been monitored in the prefailure space using resistance spectroscopy-based state space vectors, rate of change of the state variable, and acceleration of the state variable. The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss of functionality are too high to bear. Future state of the system has been estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostic health management algorithm during the vibration test has been quantified using performance evaluation metrics. The method- ology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including α − λ precision, β accuracy, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.
A remaining useful life prediction methodology for elec- trolytic capacitors is presented. This methodology adopts a Kalman filter approach in conjunction with an empirical state-based degradation model to predict the degradation of capacitor parameters through the life of the capacitor. Elec- trolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are good candi- dates for component level prognostics and health manage- ment. Prognostics provides a way to assess remaining use- ful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. This paper discusses the empirical degradation models and experi- mental results of an accelerated aging test performed on a set of identical capacitors subjected to electrical stress. The data form the basis for developing the Kalman-filter based remain- ing life prediction algorithm.
Distributed architecture for prognostics is an essential step in prognostic research in order to enable feasible real-time system health management. Communication overhead is an important design problem for such systems. In this paper we focus on communication issues faced in the distributed implementation of an important class of algorithms for prognostics – particle filters. In spite of being computation and memory intensive, particle filters lend well to distributed implementation except for one significant step – resampling. We propose new resampling scheme called parameterized resampling that attempts to reduce communication between collaborating nodes in a distributed wireless sensor network. Analysis and comparison with relevant resampling schemes are also presented. A battery health management system is used as a target application.
Electronics components have an increasingly critical role in avionics systems and in the development of future aircraft systems. Prognostics of such components is becoming a very important research field as a result of the need to provide aircraft systems with system level health management information. This paper focuses on a prognostics application for electronics components within avionics systems, and in particular its application to an Isolated Gate Bipolar Transistor (IGBT). This application utilizes the remaining useful life prediction, accomplished by employing the particle filter framework, leveraging data from accelerated aging tests on IGBTs. These tests induced thermal-electrical overstresses by applying thermal cycling to the IGBT devices. In-situ state monitoring, including measurements of steady-state voltages and currents, electrical transients, and thermal transients are recorded and used as potential precursors of failure.
This paper introduces a generic distributed prognostic health management (PHM) architecture with specific application to the electrical power systems domain. Current state-of-the-art PHM systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to loss of functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become unsuitable for successful deployment, and efficient distributed architectures are required. A distributed architecture though, is not effective unless there is an algorithmic framework to take advantage of its unique abilities. The health management paradigm envisaged here incorporates a heterogeneous set of system components monitored by a varied suite of sensors and a particle filtering (PF) framework that has the power and the flexibility to adapt to the different diagnostic and prognostic needs. Both the diagnostic and prognostic tasks are formulated as a particle filtering problem in order to explicitly represent and manage uncertainties; however, typically the complexity of the prognostic routine is higher than the computational power of one computational element (CE). Individual CEs run diagnostic routines until the system variable being monitored crosses beyond a nominal threshold, upon which it coordinates with other networked CEs to run the prognostic routine in a distributed fashion. Implementation results from a network of distributed embedded devices monitoring a prototypical aircraft electrical power system are presented, where the CEs are Sun Microsystems Small Programmable Object Technology (SPOT) devices.
This article reports on preliminary results of a study conducted to examine how temporary electrical overstress seed fault conditions in discrete power electronic components that cannot be detected with reliability tests but impact longevity of the device. These defects do not result in formal parametric failures per datasheet specifications, but result in substantial change in the electrical characteristics when compared with pristine device parameters. Tests were carried out on commercially available 600V IGBT devices using transmission line pulse (TLP) and system level ESD stress.* It was hypothesized that the ESD causes local damage during the ESD discharge which may greatly accelerate degradation mechanisms and thus reduce the life of the components. This hypothesis was explored in simulation studies where different types of damage were imposed to different parts of the device. Experimental results agree qualitatively with the simulation for a number of tests which will motivate more in-depth modeling of the damage.
Lightning induced damage is one of the major concerns in aircraft health monitoring. Such short-duration high voltages can cause significant damage to electronic devices. This paper presents a study on the effects of lightning injection on power metal-oxide semiconductor field effect transistors (MOSFETs). This approach consisted of pin- injecting lightning waveforms into the gate, drain and/or source of MOSFET devices while they were in the OFF-state. Analysis of the characteristic curves of the devices showed that for certain injection modes the devices can accumulate considerable damage rendering them inoperable. Early results demonstrate that a power MOSFET, even in its off-state, can incur considerable damage due to lightning pin injection, leading to significant deviation in its behavior and performance, and to possibly early device failures.
Understanding aging mechanisms of electronic components is of extreme importance in the aerospace domain where they are part of numerous critical subsystems including avionics. In particular, power MOSFETs are of special interest as they are involved in high voltage switching circuits such as drivers for electrical motors. With increased use of electronics in aircraft control, it becomes more important to understand the degradation of these components in aircraft specific environments. In this paper, we present an accelerated aging methodology for power MOSFETs that subject the devices to indirect thermal overstress during high voltage switching. During this accelerated aging process, two major modes of failure were observed – latch-up and die attach degradation. In this paper we present the details of our aging methodology along with details of experiments and analysis of the results.
With recent advancements in prognostics methodologies there has been a significant interest in maturing Prognostics and Health Management (PHM) to increase its technology readiness level for onboard deployments. Active research is underway both in industry and academia to address shortcomings in availability of run-to-failure data, accelerated aging environments, real-time prognostics algorithms, uncertainty representation and management (URM) techniques, prognostics performance evaluation, etc., to name a few. At this juncture it is highly desirable to close the loop by connecting the high level customer requirements for mission planning and execution to performance specifications for prognostics methodologies at the lower technical level. This calls for integrating the pragmatics of safety, reliability, cost, and real-time viability into the prognostics methodologies to establish a connection between top-down and bottom-up approaches currently pursued in the PHM community. In this paper we identify key areas that must be addressed to bridge these gaps and provide an overview of how these areas have been addressed in part at various levels. We also discuss on how these issues can be further developed into a comprehensive and more coherent portfolio of technologies that will ultimately lead to specifying guidelines for prognostics performance.
Power electronics are widely used in critical roles in modern day aircrafts and hence their health management is of great interest. An important part of prognostics and health management of these devices is understand- ing the effect of high-stress events such as lightning and how they affect their aging. In this paper we present our study and analysis of lightning injection experiments with power MOSFETs in their ON state. We show the different kind of damages that can be caused by such events and analyze their effects on device performance parameters. In addition, we present a simple yet effec- tive modeling technique that can model the degradation in these devices. Such models will play a valuable role in understanding the behavior of these damaged devices when operated under normal conditions later and sub- sequently in prognosis of their remaining useful life.We present our results on the performance of this modeling and the scope within which they can be utilized for ac- curate estimation of device damage.
Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.*
This paper presents a novel set of uncertainty measures to quantify the impact of input uncertainty on nonlinear prognosis systems. A Particle Filtering-based method is also presented that uses this set of uncertainty measures to quantify, in real time, the impact of load, environmen- tal, and other stresses for long-term prediction. Further- more, this work shows how these measures can be used to implement a novel feedback correction loop aimed to suggest modifications, at a system input level, with the purpose of extending the remaining useful life of a faulty nonlinear, non-Gaussian system. The correction scheme is tested and illustrated using real vibration feature data from a fatigue-driven fault in a critical aircraft compo- nent.
Increasing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. Systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage. Advanced adaptive controls can provide the mechanism to enable optimized operations that also provide the enabling technology for Systems Health Management goals. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency management and adaptive controls. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine.
A remaining useful life prediction algorithm and degradation model for electrolytic capacitors is presented. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management research. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. In particular, experimental results of an accelerated aging test under electrical stresses are presented. The capacitors used in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors.
Model-based prognostics approaches employ do- main knowledge about a system, its components, and how they fail through the use of physics-based models. Compo- nent wear is driven by several different degradation phenom- ena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics method- ology using particle filters, in which the problem of charac- terizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is repre- sented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model- based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active.
This paper demonstrates how to apply prognostics to power MOSFETs (metal oxide field effect transistor). The methodology uses thermal cycling to age devices and Gaussian process regression to perform prognostics. The approach is validated with experiments on 100V power MOSFETs. The failure mechanism for the stress conditions is determined to be die-attachment degradation. Change in ON-state resistance is used as a precursor of failure due to its dependence on junction temperature. The experimental data is augmented with a finite element analysis simulation that is based on a two-transistor model. The simulation assists in the interpretation of the degradation phenomena and SOA (safe operation area) change.
Battery Health Management (BHM) is a core enabling technology for the success and widespread adoption of the emerging electric vehicles of today. Although battery chemistries have been studied in detail in literature, an accurate run-time battery life prediction algorithm has eluded us. Current reliability-based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This paper presents a Particle Filter (PF) based BHM framework with plug-and-play modules for battery models and uncertainty management. The batteries are modeled at three different levels of granularity with associated uncertainty distributions, encoding the basic electrochemical processes of a Lithium-polymer battery. The effects of different choices in the model design space are explored in the context of prediction performance in an electric unmanned aerial vehicle (UAV) application with emulated flight profiles.
A remaining useful life prediction methodology for elec- trolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good can- didate for component level prognostics and health manage- ment. Prognostics provides a way to assess remaining use- ful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated ag- ing test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This prelim- inary remaining life prediction algorithm serves as a demon- stration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation pro- gression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognos- tics methods typically used for remaining useful life predic- tions in other applications.
The success of model-based approaches to systems health management depends largely on the quality of the underly- ing models. In model-based prognostics, it is especially the quality of the damage progression models, i.e., the models describing how damage evolves as the system operates, that determines the accuracy and precision of remaining useful life predictions. Several common forms of these models are gen- erally assumed in the literature, but are often not supported by physical evidence or physics-based analysis. In this paper, using a centrifugal pump as a case study, we develop differ- ent damage progression models. In simulation, we investigate how model changes influence prognostics performance. Re- sults demonstrate that, in some cases, simple damage progres- sion models are sufficient. But, in general, the results show a clear need for damage progression models that are accurate over long time horizons under varied loading conditions.
Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.
Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the state-parameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.
This article discusses several aspects of uncertainty represen- tation and management for model-based prognostics method- ologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In par- ticular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it re- lates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probabil- ity density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.
Among various approaches for implementing prognostic algorithms data-driven algorithms are popular in the industry due to their intuitive nature and relatively fast developmental cycle. However, no matter how easy it may seem, there are several pitfalls that one must watch out for while developing a data-driven prognostic algorithm. One such pitfall is the uncertainty inherent in the system. At each processing step uncertainties get compounded and can grow beyond control in predictions if not carefully managed during the various steps of the algorithms. This paper presents analysis from our preliminary development of data- driven algorithm for predicting end of discharge of Li-ion batteries using constant load experiment data and challenges faced when applying these algorithms to randomized variable loading profile as is the case in realistic applications. Lessons learned during the development phase are presented.
Electrolytic capacitors are used in several applications rang- ing from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. This makes them good candidates for prognostics and health management research. Prognostics provides a way to assess remaining use- ful life of components or systems based on their current state of health and their anticipated future use and operational con- ditions. Past experiences show that capacitors tend to degrade and fail faster under high electrical and thermal stress condi- tions that they are often subjected to during operations. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degra- dation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the de- sired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.
The focus of this work is the analysis of different degradation phenomena based on thermal overstress and electrical overstress accelerated aging systems and the use of accelerated aging techniques for prognostics algorithm development. Results on thermal overstress and electrical overstress experiments are presented. In addition, preliminary results toward the development of physics-based degradation models are presented focusing on the electrolyte evaporation failure mechanism. An empirical degradation model based on percentage capacitance loss under electrical overstress is presented and used in: (i) a Bayesian-based implementation of model-based prognostics using a discrete Kalman filter for health state estimation, and (ii) a dynamic system representation of the degradation model for forecasting and remaining useful life (RUL) estimation. A leave-one-out validation methodology is used to assess the validity of the methodology under the small sample size constrain.
The results observed on the RUL estimation are consistent through the validation tests comparing relative accuracy and prediction error. It has been observed that the inaccuracy of the model to represent the change in degradation behavior observed at the end of the test data is consistent throughout the validation tests, indicating the need of a more detailed degradation model or the use of an algorithm that could estimate model parameters on-line. Based on the observed degradation process under different stress intensity with rest periods, the need for more sophisticated degradation models is further supported. The current degradation model does not represent the capacitance recovery over rest periods following an accelerated aging stress period.
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Amazon data science challenge.
Resident of a smart home, who may be an old person or an Alzheimer patient needing permanent assistance, actuates the world by realizing activities, which are observed through the embedded sensors of smart home. Typically, this person may sometimes forget completion of the activities; may realize the activities of daily living incorrectly, and may enter to dangerous states. In order to provide automatic assistance for the smart home resident through the embedded electronically controllable actuators and make the smart home resident able to live independently at home we propose to calculate a possibilistic logical space for correct realization of activities, which may be represented in form of a multivariable problem. Regardless from the physical entity (modality and location) of the intelligence source and the quantity of individuals who perform the activities; per each possible goal or activity, we consider a unique source of intelligence (for example a social mind) who directs the order of fuzzy events that occur in the ambient environment, then the plan behind world actuations is modeled applying extensions of the fuzzy logic. The main key point that we deal with is the analysis of the observations in order to make inferences about possible simultaneous activities that may be planned and realized by one or more individuals; so that we can reason in the cases the parallel activities are interrupted.
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water recycling system svg's
A water recycling system (WRS) deployed at NASA Ames Research Center’s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned out- ages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.
Systems health management (SHM) is an impor- tant set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL predic- tion. We solve these model decomposition problems using a three-tank system as a case study.
Systems health monitoring is essential in guar- anteeing the safe, efficient, and correct opera- tion of complex engineered systems. Diagnosis, which consists of detection, isolation and identi- fication of faults; and prognosis, which consists of prediction of the remaining useful life of com- ponents, subsystems, or systems; constitute sys- tems health monitoring. This paper presents an integrated model-based diagnostic and prognos- tic framework, where we make use of a com- mon modeling paradigm to model both the nom- inal and faulty behavior in all aspects of systems health monitoring. We illustrate our approach us- ing a simulated propellant loading system that in- cludes tanks, valves, and pumps.
This paper presents a distributed Bayesian fault diagnosis scheme for physical systems. Our diagnoser design is based on a procedure for factoring the global system bond graph (BG) into a set of structurally observable bond graph fac- tors (BG-Fs). Each BG-F is systematically translated into a corresponding DBN Factor (DBN-F), which is then used in its corresponding local diagnoser for quantitative fault detec- tion, isolation, and identification. By construction, the ran- dom variables in each DBN-F are conditionally independent of the random variables in all other DBN-Fs, given a subset of communicated measurements considered as system inputs. Each DBN-F and BG-F pair is used to derive a local diag- noser that generates globally correct diagnosis results by lo- cal analysis. Together, the local diagnosers diagnose all single faults of interest in the system. We demonstrate on an electri- cal system how our distributed diagnosis scheme is compu- tationally more efficient than its centralized counterpart, but without compromising the accuracy of the diagnosis results.
This paper introduces a novel reasoning methodology, in combination with appropriate models and measurements (data) to perform accurately and expeditiously expert troubleshooting for complex military and industrial processes. This automated troubleshooting tool is designed to support the maintainer/ repairman by identifying and locating faulty system components. The enabling technologies build upon a Model Based Reasoning paradigm and a Dynamic Case Based Reasoning method acting as the intelligent database. A case study employs a helicopter Intermediate Gearbox as the application domain to illustrate the efficacy of the approach.
Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key tech- nology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component life- times that are important, but, rather, the lifetimes of the sys- tems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the rela- tive lack of scalability and efficiency of typical prognostics approaches. In order to address these issues, we develop a distributed solution to the system-level prognostics prob- lem, based on the concept of structural model decomposi- tion. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resulting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The re- sults show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.
Diagnosis and prognosis are necessary tasks for system re- configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents a novel inte- grated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automati- cally constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four- wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.
Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.
Model-based diagnosis enables efficient and safe operation of engineered systems. In this paper, we describe two algorithms based on a qualitative event-based fault isolation framework augmented with model-based fault identification that are applied to spacecraft power distribution systems. Although based on a common framework, the fundamental difference between the two algorithms is that one uses a global model for residual generation, fault isolation, and fault identification; whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe the implementation of the two algorithms and compare their diagnosis results on a representative spacecraft power distribution system.
Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec- tion, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents an integrated model-based distributed diagnosis and prognosis framework, where system decomposi- tion is used to perform the diagnosis and prog- nosis tasks in a distributed way. We show how different submodels can be automatically con- structed to solve the local diagnosis and prog- nosis problems. We illustrate our approach us- ing a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs fault diagnosis and prognosis in a robust manner.
We present the third implementation of a framework created jointly by NASA Ames Research Center, Palo Alto Research Center, and Delft University of Technology to com- pare and evaluate diagnosis algorithms (DAs). This year‟s competition, DXC‟11, introduces a software track in addition to the industrial and synthetic tracks of previous competitions. A total of eleven DAs competed in the three tracks. The paper describes the systems, diag- nostic problems of the tracks, fault scenarios, evaluation metrics, participating DAs, results and analysis.
We describe two model-based diagnosis algo- rithms entered into the Third International Diag- nostic Competition. We focus on the first diag- nostic problem of the industrial track of the com- petition in which a diagnosis algorithm must de- tect, isolate, and identify faults in an electrical power distribution testbed in order to provide cor- rect abort recommendations. Both diagnosis al- gorithms are based on a qualitative event-based fault isolation framework augmented with model- based fault identification. Although based on a common framework, the fundamental difference between the two algorithms is that one is based on a global model for residual generation, fault iso- lation, and fault identification, whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe, compare, and contrast the two algorithms in terms of practical implementation and their diagnosis results.
Complex engineering systems require efficient fault diagnosis methodologies, but centralized ap- proaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decompo- sition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals, computed by extending Possible Conflicts, to build local event-based diagnosers based on global diagnosability analysis that gen- erate globally correct local diagnosis results. The proposed approach is applied to a multi-tank sys- tem, and results demonstrate an improvement in the design of local diagnosers. Since local diag- nosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed dis- tributed approaches.
Model-based prognostics approaches capture system knowl- edge in the form of physics-based models of components that include how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decom- position approach adapted from the diagnosis community, called possible conflicts, in order to both improve the com- putational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state esti- mate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation- based experiments to demonstrate the approach.
The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are neces- sary to quickly identify when a fault occurs, so that recov- ery actions can be taken or an abort procedure can be initi- ated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physi- cal processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their ef- fects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which de- scribes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the in- fluence of different faults on this process. The model takes into account the main physical processes such as highly non- equilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydro- gen and vapor flows inside the system in the presence of he- lium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based di- agnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and per- form fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.
This paper describes how damage propagation can be tracked and modeled for a range of fault modes in some modules of commercial high bypass aircraft engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine (cycle deck) as a function of variations of flow and efficiency of the modules of interest. These surfaces are normalized and superimposed. Next, sensor readings are matched to those surfaces and – using an optimization approach – the corresponding flow and efficiency pair is found that best explains the sensor data. This flow and efficiency pair is then compared to previous pairs and the direction of the change as well as the rate of change is determined. The whole trajectory is then projected into the time domain. An extrapolation of the curve to the limit (which is established via operational margins) yields the remaining life. In a backward mode, the extrapolated curve is discretized and estimated future flow and efficiency pairs are retrieved. These pairs are then input to the cycle deck to produce future anticipated sensor readings as well as confirmatory trips of operational margins. Changes of the future sensor readings with real readings are used to adjust the remaining life calculations. The method is demonstrated on time series of historical engine faults.
Prognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenancehave been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.
Prognostics has taken center stage in Condition Based Maintenance (CBM) where it is desired to estimate Remaining Useful Life (RUL) of a system so that remedial measures may be taken in advance to avoid catastrophic events or unwanted downtimes. Validation of such predictions is an important but difficult proposition and a lack of appropriate evaluation methods renders prognostics meaningless. Evaluation methods currently used in the research community are not standardized and in many cases do not sufficiently assess key performance aspects expected out of a prognostics algorithm. In this paper we introduce several new evaluation metrics tailored for prognostics and show that they can effectively evaluate various algorithms as compared to other conventional metrics. Four prognostic algorithms, Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Polynomial Regression (PR), are compared. These algorithms vary in complexity and their ability to manage uncertainty around predicted estimates. Results show that the new metrics rank these algorithms in a different manner; depending on the requirements and constraints suitable metrics may be chosen. Beyond these results, this paper offers ideas about how metrics suitable to prognostics may be designed so that the evaluation procedure can be standardized.
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison.
Composite structures are gaining importance for use in the aerospace industry. Compared to metallic structures their behavior is less well understood. This lack of understanding may pose constraints on their use. One possible way to deal with some of the risks associated with potential failure is to perform in-situ monitoring to detect precursors of failures. Prognostic algorithms can be used to predict impending failures. They require large amounts of training data to build and tune damage model for making useful predictions. One of the key aspects is to get confirmatory feedback from data as damage progresses. These kinds of data are rarely available from actual systems. The next possible resource to collect such data is an accelerated aging platform. To that end this paper describes a fatigue cycling experiment with the goal to stress carbon-carbon composite coupons with various layups. Piezoelectric disc sensors were used to periodically interrogate the system. Analysis showed distinct differences in the signatures of growing failures between data collected at conditions. Periodic X-radiographs were taken to assess the damage ground truth. Results after signal processing showed clear trends of damage growth that were correlated to damage assessed from the X-ray images.
Smart Home resident may be an Alzheimer patient needing continuous assistance and care giving. Because of forgetfulness, this person may realize activities of daily living erroneously. In order to assist this person automatically in Smart Home, all his performed actions and activities are observed through the embedded sensors of Smart Home, and applying the data mining techniques his activities are analyzed. Then information about his activities is provided and in the consequence, comparing learned correct patterns and current observations the Smart Home may infer provision of assistance to this person at the appropriate moment. In this paper we propose a data-driven activity modeling approach, which supports reasoning in correct realization of the activities. Activities are presumed as the series of fuzzy events that occur shortly one after another. Per each activity, we calculate a fuzzy conceptual structure, and the model of activity is represented in form of a multivariable problem.
Sample report in support of "Discovering
Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms" manuscript.
not available
This page is the repository for the publications resulting from the AePW.  This includes the special sessions at conferences:

AIAA ASM 2012, Grapevine TX;

AIAA SDM 2012, Boston;

RAES IFASD 2012, Bristol UK;  

Also included:

ICCFD 2012, Hawaii;

USNCCM 2013, Raleigh
The databases of computational and experimental data from the first Aeroelastic Prediction Workshop are located here.

The databases file names tell their contents by configuration, angle of attack, Mach number and Reynolds number where necessary.  The experimental data sets are in files with _X in the name.  Files without an _X are computational results.

These are the files updated to include data received as of Sept 11, 2013. (JH)
Images for the website template go here.  It will not change their names or locations, but will hopefully help to organize them.  Oh, but for a directory structure...
Images for RSW configuration.
image files for the BSCW configuration webpages.
images for the HIRENASD webpage
This data set deals with Maintenance Action Recommendations
This data set contains numerous trajectories of engine related parameters that terminate at a lower acceptable threshold
Future NASA plans call for long-duration deep space
missions with human crews. Because of light-time delay and
other considerations, increased autonomy will be needed.
This will necessitate integration of tools in such areas as
anomaly detection, diagnosis, planning, and execution. In
this paper we investigate an approach that integrates planning
and execution by embedding planner-derived temporal
constraints in an execution procedure. To avoid the need for
propagation, we convert the temporal constraints to
dispatchable form. We handle some uncertainty in the
durations without it affecting the execution; larger variations
may cause activities to be skipped.
The goal of prognostics and health management (PHM) systems is to ensure system safety, and reduce downtime and maintenance costs. It is important that a PHM system is verified and validated before it can be successfully deployed. Prognostics algorithms are integral parts of PHM systems. This paper investigates a systematic process of verification of such prognostics algorithms. To this end, first, this paper distinguishes between technology maturation and product development. Then, the paper describes the verification process for a prognostics algorithm as it moves up to higher maturity levels. This process is shown to be an iterative process where verification activities are interleaved with validation activities at each maturation level. In this work, we adopt the concept of technology readiness levels (TRLs) to represent the different maturity levels of a prognostics algorithm. It is shown that at each TRL, the verification of a prognostics algorithm depends on verifying the different components of the algorithm according to the requirements laid out by the PHM system that adopts this prognostics algorithm. Finally, using simplified examples, the systematic process for verifying a prognostics algorithm is demonstrated as the prognostics algorithm moves up TRLs.
Unmanned aircraft systems (UAS) can be used for scientific, emergency management, and defense missions, among others. The existing federal air regulations, procedures, and technologies do not allow routine UAS access to the National Airspace System (NAS), with the UAS being flown primarily within restricted airspaces. The current Certificate of Waiver of Authorization (COA) process requirement for UAS operations in the NAS are extremely resource intensive, lengthy, and often lacks the flexibility to meet the full mission needs. As the number of UAS operations increases, new methodologies will be needed to enable their safe and routine access to both restricted and unrestricted airspace in the NAS. This paper focuses on gaining a better understanding of growth of NAS usage in near-term NAS UAS demand in that airspace, and an assessment of the impact of unrestricted UAS deployment in the NAS that may facilitate the development of enabling methodologies. Using software simulations for demand growth generation and NAS operations the impact of UAS integration into the NextGen NAS is simulated to analyze its impact on the delay, congestion, loss of separation conflicts, fuel burn, and noise level. Our analyses show that while there is a slight increase in these factors due to additional UAS flight, this increase is minimal compared to the levels caused by the increase of commercial traffic alone.
This paper presents an experimental study of damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in situ non-destructive evaluation (NDE) during fatigue cyclical loading. PZT wafers are used to monitor the wave reflection from the boundaries of the fatigue crack at the edge of bolt joints. The group velocity of the guided wave is calculated to select a proper time window in which the received signal contains the damage information. It is found that the fatigue crack lengths are correlated with three main features of the signal, i.e., correlation coefficient, amplitude change, and phase change. It was also observed that a single feature cannot be used to quantify the damage among different specimens since a considerable variability was observed in the response from different specimens. A multi-feature integration method based on a second-order multivariate regression analysis is proposed for the prediction of fatigue crack lengths using sensor measurements. The model parameters are obtained using training datasets from five specimens. The effectiveness of the proposed methodology is demonstrated using several lap joint specimens from different manufactures and under different loading conditions.
Health management (HM) technologies have been employed for safety critical system for decades, but a coherent systematic process to integrate HM into the system design is not yet clear. Consequently, in most cases, health management resorts to be an after-thought or ‘band-aid’ solution. Moreover, limited guidance exists for carrying out systems engineering (SE) on the subject of writing requirements for designs with integrated vehicle health management (IVHM). It is well accepted that requirements are key to developing a successful IVHM system right from the concept stage to development, verification, utilization, and support. However, writing requirements for systems with IVHM capability have unique challenges that require the designers to look beyond their own domains and consider the constraints and specifications of other interlinked systems. In this paper we look at various stages in the SE process and identify activities specific to IVHM design and development. More importantly, several relevant questions are posed that system engineers must address at various design and development stages. Addressing these questions should provide some guidance to systems engineers towards writing IVHM related requirements to ensure that appropriate IVHM functions are built into the system design
In this work, a prognostics framework to predict the evolution of damage in fiber-reinforced composites materials under fatigue loads is proposed. The assessment of internal damage thresholds is a challenge for fatigue prognostics in composites due to inherent uncertainties, existence of multiple damage modes, and their complex interactions. Our framework, considers predicting the balance of release strain energies from competing damage modes to establish a reference threshold for prognostics. The approach is demonstrated on data collected from a run-to-failure tension-tension fatigue experiment measuring the evolution of fatigue damage in carbon-fiber-reinforced polymer (CFRP) cross-ply laminates. Results are presented for the prediction of expected degradation by micro-cracks for a given panel with the associated uncertainty estimates.
In this paper, a probabilistic delamination location and size detection framework is proposed. The delamination probability image using Lamb wave-based damage detection is constructed using the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using Bayesian updating (Bayesian Imaging Method - BIM) is proposed. Following this, the composite coupon fatigue testing setup is introduced and the corresponding lamb wave diagnosis signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method to detect delamination size and location, as well as their confidence bounds. The damage detection results using the proposed methodology are compared with X-ray images for verification and validation. Finally, some conclusions and future works are drawn based on the proposed study.
In this work, a framework for the estimation of the fatigue damage propagation in CFRP composites is proposed. Macro-scale phenomena such as stiffness and strength degradation are predicted by connecting micro-scale and macro-scale damage models in a Bayesian filtering framework that also allows incorporating uncertainties in the prediction. The approach is demonstrated on data collected from a run-to-failure tension-tension fatigue experiment measuring the evolution of fatigue damage in CRFP cross-ply laminates. Results are presented for the prediction of expected end of life for a given panel with the associated uncertainty estimates.
The term Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable sustainable and safe operation of components and subsystems within aerospace platforms. However, very little guidance exists for the systems engineering aspects of design with IVHM in mind. It is probably because of this that designers have to use knowledge picked up exclusively by experience rather than by established process. This motivated a group of leading IVHM practitioners within the aerospace industry under the aegis of SAE’s HM-1 technical committee to author a document that hopes to give working engineers and program managers clear guidance on all the elements of IVHM that they need to consider before designing a system. This proposed recommended practice (ARP6883 [1]) will describe all the steps of requirements generation and management as it applies to IVHM systems, and demonstrate these with a “real-world” example related to designing a landing gear system. The team hopes that this paper and presentation will help start a dialog with the larger aerospace community and that the feedback can be used to improve the ARP and subsequently the practice of IVHM from a systems engineering point-of-view.
This paper presents a model-driven methodology for predict- ing the remaining useful life of electrolytic capacitors. This methodology adopts a Kalman filter approach in conjunction with an empirical state-based degradation model to predict the degradation of capacitor parameters through the life of the capacitor. Electrolytic capacitors are important components of systems that range from power supplies on critical avion- ics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their critical role in the system, they are good candidates for component level prognostics and health management. Prognostics provides a way to assess remain- ing useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. This paper proposes and empirical degradation model and discusses experimental results for an accelerated aging test performed on a set of identical capacitors subjected to electrical stress. The data forms the basis for developing the Kalman-filter based remaining life prediction algorithm.
A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.
The prognostic technique for a power MOSFET presented
in this paper is based on accelerated aging of MOSFET
IRF520Npbf in a TO-220 package. The methodology utilizes
thermal and power cycling to accelerate the life of the devices.
The major failure mechanism for the stress conditions is die attachment
degradation, typical for discrete devices with lead free
solder die attachment. It has been determined that die attach
degradation results in an increase in ON-state resistance
due to its dependence on junction temperature. Increasing
resistance, thus, can be used as a precursor of failure for the
die-attach failure mechanism under thermal stress. A feature
based on normalized ON-resistance is computed from in-situ
measurements of the electro-thermal response. An Extended
Kalman filter is used as a model-based prognostics techniques
based on the Bayesian tracking framework.
In this paper, we present an architecture and a formal framework to be used for systematic benchmarking of
monitoring and diagnostic systems and for producing comparable performance assessments of different diagnostic
technologies. The framework defines a number of standardized specifications, which include a fault catalog, a library of modular test scenarios, and a common protocol for gathering and processing diagnostic data. At the center of the framework are 13
benchmarking metric definitions. The calculation of metrics is
illustrated on a probabilistic model-based diagnosis algorithm
utilizing Bayesian reasoning techniques. The diagnosed system is
a real-world electrical power system, namely the Advanced
Diagnostics and Prognostics Testbed (ADAPT) developed and
located at the NASA Ames Research Center. The proposed
benchmarking approach shows how to generate realistic
diagnostic data sets for large-scale, complex engineering systems,
and how the generated experimental data can be used to enable
“apples to apples” assessments of the effectiveness of different
diagnostic and monitoring algorithms.
Electrical plug loads comprise an increasingly larger share of building energy consumption as improvements have been made to Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems. It is anticipated that plug loads will account for a significant portion of the energy consumption of Sustainability Base, a recently constructed high-performance office building at NASA Ames Research Center. Consequently, monitoring plug loads will be critical to achieve energy efficient operations. In this paper we describe the development of a knowledge-based system to analyze data collected from a plug load management system that allows for metering and control of individual loads. Since Sustainability Base was not yet occupied at the time of this investigation, the study was conducted in another building on the Ames campus to prototype the system. The paper focuses on the knowledge engineering and verification of a modular software system that promotes efficient use of office building plug loads. The knowledge-based system generates summary usage reports and alerts building personnel of malfunctioning equipment and unexpected plug load consumption. The system is planned to be applied to Sustainability Base and is expected to identify malfunctioning loads and reduce building energy consumption.
NASA Ames Research Center’s Sustainability Base is a new
50,000 sq. ft. LEED Platinum office building. Plug loads
are expected to account for a significant portion of the overall
energy consumption. This is because building design choices
have resulted in greatly reduced energy demand from Heating,
Ventilation, and Air Conditioning (HVAC) and lighting
systems, which are major contributors to energy consumption
in traditional buildings. In anticipation of the importance of
plug loads in Sustainability Base, a pilot study was conducted
to collect data from a variety of plug loads. A number of cases
of anomalous or unhealthy behavior were observed including
schedule-based rule failures, time-to-standby errors, changed
loads, and inter-channel anomalies. These issues prevent effective
plug load management; therefore, they are important
to promptly identify and correct. The Inductive Monitoring
System (IMS) data mining algorithm was chosen to identify
errors. This paper details how an automated data analysis program
was created, tested and implemented using IMS. This
program will be applied to Sustainability Base to maintain
effective plug load management system performance, identify
malfunctioning equipment, and reduce building energy
consumption.
NASA Ames Research Center’s Sustainability Base
is a new 50,000 sq. ft. high-performance office building targeting
a LEED Platinum rating. Plug loads are expected to account for a
significant portion of overall energy consumption because building
design choices resulted in greatly reduced energy demand
from Heating, Ventilation, and Air Conditioning (HVAC) and
lighting systems, which are typically major contributors to energy
consumption in traditional buildings. This paper reports on a
pilot study where data from a variety of plug loads were collected
in a reference office building to understand usage patterns,
to make a preliminary assessment as to the effectiveness of
controlling (i.e., turning off and on) selected loads, and to evaluate
the utility of the plug load management system chosen for the
study. Findings indicate that choosing energy efficient equipment,
ensuring that power saving functionality is operating effectively,
promoting beneficial occupant energy behavior, and employing
plug load controls to turn off equipment when not in use can
lead to significant energy savings. These recommendations will
be applied to Sustainability Base and further studies of plug
load management systems and techniques to reduce plug energy
consumption will be pursued.
An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die-attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
One of the most prominent technical challenges to 
effective deployment of health management systems is the 
vast difference in user objectives with respect to 
engineering development. In this paper, a detailed survey 
on the objectives of different users of health management 
systems is presented. These user objectives are then 
mapped to the metrics typically encountered in the 
development and testing of two main systems health 
management functions: diagnosis and prognosis. Using 
this mapping, the gaps between user goals and the metrics 
associated with diagnostics and prognostics are identified 
and presented with a collection of lessons learned from 
previous studies that include both industrial and military 
aerospace applications.*
Electrical power systems play a critical role in spacecraft
and aircraft, and they exhibit a rich variety of failure modes.
This paper discusses electrical power system fault diagnosis
by means of probabilistic techniques. Specically, we discuss
our development of a diagnostic capability for an electrical
power system testbed, ADAPT, located at NASA Ames.
We emphasize how we have tackled two challenges, regarding
modelling and real-time performance, often encountered
when developing diagnostic applications. We carefully discuss
our Bayesian network modeling approach for electrical
power systems. To achieve real-time performance, we build
on recent theoretically well-founded developments that compile
a Bayesian network into an arithmetic circuit. Arithmetic
circuits have low footprint and are optimized for embedded,
real-time systems such as spacecraft and aircraft.
We discuss our probabilistic diagnostic models developed for
ADAPT along with successful experimental results.
This paper presents a formal computational
methodology to explain how the oxide in
semiconductors degrades over time and the dependence
of oxide degradation on voltage and temperature
stresses. The effects of aging are modeled and
quantified by modification of the gate-source
capacitance value. The model output is validated using
experimental results of a thermally aged power
semiconductor device.
Smart lighting systems in low energy commercial buildings can be expensive to implement and commission. Studies have also shown that only 50% of these systems are used after installation, and those used are not operated at full capacity due to inadequate commissioning and lack of personalization. Wireless sensor networks (WSN) have great potential to enable personalized smart lighting systems for real-time model predictive control of integrated smart building systems. In this paper we present a framework for using a WSN to develop a real-time indoor lighting inverse model as a piecewise linear function of window and artificial light levels, discretized by sub-hourly sun angles. Applied on two days of daylight and ten days of artificial light data, this model was able to predict the light level at seven monitored workstations with accuracy sufficient for daylight harvesting and lighting control around fixed work surfaces. The reduced order model was also designed to be used for long term evaluation of energy and comfort performance of the predictive control algorithms. This paper describes a WSN experiment from an implementation at the Sustainability Base at NASA Ames, a living laboratory that offers opportunities to test and validate information-centric smart building control systems.
This paper demonstrates a novel optimizationbased
approach to estimating fault states in a DC power
system. The model includes faults changing the circuit topology
along with sensor faults. Our approach can be considered as
a relaxation of the mixed estimation problem. We develop
a linear model of the circuit and pose a convex problem
for estimating the faults and other hidden states. A sparse
fault vector solution is computed by using l1 regularization.
The solution is computed reliably and efficiently, and gives
accurate diagnostics on the faults. We demonstrate a real-time
implementation of the approach for an instrumented electrical
power system testbed at NASA. Accurate estimates of multiple
faults are computed in milliseconds on a PC. The approach
performs well despite unmodeled transients and other modeling
uncertainties present in the system.
This paper presents a new sensing application
to diagnose power semiconductor aging in power drive
systems. It has been shown previously that device
parasitic characteristics change during the aging process
which results in detectable changes in their frequency
response. This change is manifested in the current signal
at very high frequencies. Therefore, using a wideband
AC current sensor, high frequency components of the
current can be acquired, providing a way to detect
device aging.
Many diagnostic datasets suffer from the adverse
effects of spikes that are embedded in data and noise. For
example, this is true for electrical power system data where
the switches, relays, and inverters are major contributors to
these effects. Spikes are mostly harmful to the analysis of
data in that they throw off real-time detection of abnormal
conditions, and classification of faults. Since noise and
spikes are mixed together and embedded within the data,
removal of the unwanted signals from the data is not always
easy and may result in losing the integrity of the
information carried by the data. Additionally, in some
applications noise and spikes need to be filtered
independently. The proposed algorithm is a multi-resolution
filtering approach based on Haar wavelets that is capable of
removing spikes while incurring insignificant damage to
other data. In particular, noise in the data, which is a useful
indicator that a sensor is healthy and not stuck, can be
preserved using our approach. Presented here is the
theoretical background with some examples from a realistic
testbed.
Software-in-the-loop and hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The offline testing framework is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle.
Validation of prognostic technologies through ground and flight tests is an important step in maturing these novel technologies and deploying them on real-world systems. To this end, a series of flight tests have been conducted using an unmanned electric vehicle during which the motor system batteries were monitored by a prognostic algorithm. The research presented here endeavors to produce and validate a technology for predicting the remaining time until end-ofdischarge of the batteries on an electric aircraft as a function of an expected future flight and online estimates of the charge contained in the batteries. Flight data and flight experiment results are presented along with an assessment of model and algorithm performance
This paper investigates a non-traditional use of differential current sensor and current sensor to detect intermittent disconnection problems in connectors. An intermittent disconnect, often resulting in an arc, creates an imbalance which is manifested in the current. The traveling wave generated due to the perturbation can be detected using current sensors. This paper shows the feasibility to detect disconnection based on this principle.
The field of Prognostic Health Management (PHM) has been undergoing rapid growth in recent years, with development of increasingly sophisticated techniques for diagnosing faults in system components and estimating fault progression tra- jectories. Research efforts on how to utilize prognostic health information (e.g. for extending the remaining useful life of the system, increasing safety, or maximizing operational ef- fectiveness) are mostly in their early stages, however. This process of using prognostic information to determine a sys- tem’s actions or its configuration is beginning to be referred to as Prognostic Decision Making (PDM). In this paper we, first, propose a formulation of the PDM problem with the at- tributes of the aerospace domain in mind, outline some of the key requirements on PDM methods, and explore techniques that can be used as a foundation of PDM development. The problem of Pareto set viability, i.e. satisfaction of perfor- mance goals set for objective functions, is discussed next, followed by ideas for possible solutions. The ideas, termed Dynamic Constraint Redesign (DCR), have roots in the fields of Multidisciplinary Design Optimization and Game Theory. Prototype PDM and DCR algorithms are also described and results of their testing are presented.
This paper presents a new on-line methodology for detecting intermittent disconnection failures. The detection principle operates on the fundamental Lorentz Law that states that sudden changes in flux create a large voltage, resulting in an arc. This arc propagates as a traveling wave through the circuit until all the energy associated with it is dissipated. It is possible to detect that traveling wave as an indication of an intermittent disconnection failure. A test bench was implemented to test and validate the theory. Preliminary results presented in this paper show the feasibility of detecting disconnection failures and the possibility of locating the failing connector based on distance.
The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in PDM is finding a sufficiently expressive yet compact mathematical representation of the system for use with decision optimization algorithms. In this paper we describe a general modeling approach for a class of PDM problems with non-linear system degradation processes and uncertainties in state estimation, action effects, and future operating conditions. The approach is based on continuous Partially Observable Markov Decision Processes (POMDPs) used in conjunction with 'black box' system simulations. The proposed modeling framework can be cast into simpler representations, depending on which sources of uncertainty are being included. The approach is illustrated with a mission planning case study for an unmanned aerial vehicle (UAV). In the case study a PDM system is tasked with optimizing the vehicle route after an in-flight component fault is detected. A stochastic algorithm (based on particle filtering) is used for decision optimization, with a second, deterministic algorithm providing a performance evaluation baseline. Both algorithms utilize a UAV physics simulator for generating predictions of future vehicle states. Performance benchmarking is done on a set of mission scenarios of increasing complexity.
As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.
A reduced dynamical model describing temperature stratification effects driven by natural convection in a liquid hydrogen cryogenic fuel tank has been developed. It accounts for cryogenic propellant loading, storage, and unloading in the conditions of normal, increased, and micro- gravity. The model involves multiple horizontal control volumes in both liquid and ullage spaces. Temperature and velocity boundary layers at the tank walls are taken into account by using correlation relations. Heat exchange involving the tank wall is considered by means of the lumped-parameter method. By employing basic conservation laws, the model takes into consideration the major multi-phase mass and energy exchange processes involved, such as condensation-evaporation of the hydrogen, as well as flows of hydrogen liquid and vapor in the presence of pressurizing helium gas. The model involves a liquid hydrogen feed line and a tank ullage vent valve for pressure control. The temperature stratification effects are investigated, including in the presence of vent valve oscillations. A simulation of temperature stratification effects in a generic cryogenic tank has been implemented in Matlab and results are presented for various tank conditions.
A dynamical model describing the multi-stage process of rocket propellant loading has been developed. It accounts for both the nominal and faulty regimes of cryogenic fuel loading when liquid hydrogen is moved from a storage tank to an external tank via a transfer line. By employing basic conservation laws, the reduced, lumped-parameter model takes into consideration the major multi-phase mass and energy exchange processes involved, such as highly non-equilibrium condensation-evaporation of hydrogen, pressurization of the tanks, and liquid hydrogen and hydrogen vapor flows in the presence of pressurizing helium gas. A self-consistent theory of dynamical condensation-evaporation has been developed that incorporates heat flow by both conduction and convection through the liquid-vapor interface inside the tanks. A simulation has been developed in Matlab for a generic refueling system that involves the solution of a system of ordinary integro-differential equations. The results of these simulations are in good agreement with the Space Shuttle refueling data.
Diagnosability is an important issue in the design of diagnostic systems, because it helps identify whether sufficient information is available to distinguish all the faults. Diagnosability of hybrid systems, however, is challenging, because mode transitions may occur during fault isolation. We present an event-based framework for hybrid systems diagnosis based on a qualitative abstraction of measurement deviations from nominal behavior. We derive event-based fault models that describe the possible measurement deviations sequences due to faults, which, coupled with the mode transition structure of the system, are used to automatically synthesize an event-based diagnoser for hybrid systems. We introduce notions of diagnosability for hybrid systems and show how the event-based diagnoser can be used to verify the diagnosability of the system. We apply our diagnosability analysis scheme to a real-world electrical power distribution system.
Fault diagnosis is key to ensuring system safety through fault-adaptive control. This task is diffcult in hybrid systems with combined continuous and discrete behaviors because mode changes make diagnosability hard to achieve. Including additional sensors can improve diagnosability, but that is not always feasible. An alternative strategy is active diagnosis, where we improve the diagnosis result by executing or blocking controllable events. We present a qualitative, event-based approach to active diagnosis of hybrid systems, where we automatically synthesize event-based diagnosers for hybrid systems that can determine if the system is diagnosable through passive or active diagnosis. We apply our active diagnosis scheme to a real-world electrical power distribution system.
Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.
Increasing demand for improved reliability and survivability of mission-critical systems is driving the
development of health monitoring and Automated Contingency Management (ACM) systems. An ACM system is expected to adapt autonomously to fault conditions with the goal of still
achieving mission objectives by allowing some degradation in system performance within permissible limits. ACM performance depends on supporting technologies like sensors
and anomaly detection, diagnostic/prognostic and reasoning algorithms. This paper presents the development of a generic prototype test bench software framework for developing and
validating ACM systems for advanced propulsion systems called the Propulsion ACM (PACM) Test Bench. The
architecture has been implemented for a Monopropellant Propulsion System (MPS) to demonstrate the validity of the approach. A Simulink model of the MPS has been developed
along with a fault injection module. It has been shown that the ACM system is capable of mitigating the failures by searching for an optimal strategy. Furthermore, few relevant
experiments have been presented to show proof of concepts.
This paper introduces a novel Prognostics-enhanced Automated Contingency Management (or ACM+P) paradigm based on both current health state (diagnosis) and future health state estimates (prognosis) for advanced autonomous systems. Including prognostics in ACM system allows not only fault accommodation, but also fault mitigation via proper control actions based on short term prognosis, and moreover, the establishment of a long term operational plan that optimizes the
utility of the entire system based on long term prognostics. Technical challenges are identified and addressed by a hierarchical ACM+P architecture that allows fault accommodation and mitigation at various levels in the system ranging from component level control reconfiguration, system level control reconfiguration, to high level mission re-planning and resource redistribution. The ACM+P paradigm was developed and evaluated in a high fidelity Unmanned Aerial Vehicle (UAV) simulation environment with flight-proven baseline flight controller and simulated diagnostics and prognostics of flight control actuators. Simulation results are presented. The ACM+P concept, architecture and the generic methodologies presented in this paper are applicable to many advanced autonomous systems such as deep space probes, unmanned autonomous vehicles, and military and commercial aircraft.
Precursor parameters have been identified to enable development of a prognostic approach for insulated gate bipolar transistors (IGBT). The IGBT were subjected to thermal overstress tests using a transistor test board until device latch-up. The collector-emitter current, transistor case temperature, transient and steady state gate voltages, and transient and steady state collector-emitter voltages were monitored in-situ during the test. Pre- and post-aging characterization tests were performed on the IGBT. The aged parts were observed to have shifts in capaci- tance-voltage (C-V) measurements as a result of trapped charge in the gate oxide. The collector-emitter ON voltage V_CE(ON) showed a reduction with aging. The reduction in the V_CE(ON) was found to be correlated to die attach degradation, as observed by scan- ning acoustic microscopy (SAM) analysis. The collector-emitter voltage, and transistor turn-off time were observed to be precursor parameters to latch-up. The monitoring of these precursor parameters will enable the development of a prognostic methodology for IGBT failure. The prognostic methodology will involve trending precursor data, and using physics of failure models for prediction of the remaining useful life of these devices.
As part of an effort to monitor electricity usage by plug loads in a new high performance office building, plug load management devices were deployed to enable data collection, analysis, and active control of plug loads. We used a Commercial Off-The-Shelf (COTS) plug load management system to capture relevant data for two different types of multi-function devices (MFDs) in the facility, one of which was tested for use with different power settings. This enabled a quantitative analysis to assess impacts on energy consumption. It was found that a projected 65% reduction in annual energy consumption would result by using a newer, Energy Star compliant model of MFD, and an additional projected 39% reduction in annual energy consumption would result by subsequently changing the time-to-sleep for that MFD. It was also found that it may be beneficial to apply automated analysis with anomaly detection algorithms to detect problems with MFD performance, such as a failure to go to sleep mode or variations in sleep power draw. Furthermore, we observed that energy savings realized by using plug load management devices to de-energize (unplug) MFDs during non-business hours depends on the sleep power draw and time-to-sleep setting. For the MFDs in this study with settings established per the maintenance contract (which were different than factory default values), turning the device off at night and then on in the morning used more energy than leaving it on in sleep mode due to the start-up behavior and excessive time-to-sleep setting of four hours. From this and other assessments, we offer these recommendations to building occupants: reduce MFD time-to-sleep, encourage employees to use the power save button, and apply automated analysis to detect problems with device performance.
Answers to frequently asked questions will be posted here for the benefit of all users of this data. Questions posed in the future may also be incorporated into the document.
Abstract—Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The
comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in
regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology
studies.
not available
Dataset from the NASA ASRS from Light GA Aircraft - looking for fuel exhaustion fuel starvation fuel gauge engine rough engine stopped
Nominal scenarios (laboratory)
Nominal and ballscrew jam fault scenarios. Includes extended 2-min scenarios.
Ballscrew jam fault datasets (diagnostic and prognostic)
Nominal and ballscrew jam fault datasets (diagnostic and prognostic)
Ballscrew jam fault datasets (diagnostic and prognostic)
Jam fault scenarios with tensile (-40 lbs) load (diagnostic and prognostic).
Jam fault scenarios with tensile (+40 lbs) load (diagnostic and prognostic)
Scenarios with a non-functioning position sensor fault (diagnostic)
Ballscrew jam fault datasets (diagnostic and prognostic)
Ballscrew jam fault datasets (diagnostic and prognostic)
Ballscrew jam fault datasets (diagnostic and prognostic)
Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system, and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into computationally-independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Computationally independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability.
Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural model decomposition method from the Artificial Intelligence model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.
The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.
The National Airspace System (NAS) is an ever changing and complex engineering system. As the Next Generation Air Transportation System (NextGen) is developed, there will be an increased emphasis on safety and operational and environmental efficiency. Current operations in the NAS are
monitored using a variety of data sources, including data from flight recorders, radar track data, weather data, and other massive data collection systems. Although numerous technologies exist to
monitor the frequency of known but undesirable behaviors in the NAS, there are currently few methods that can analyze the large repositories to discover new and previously unknown events in the NAS. Having a tool to discover events that have implications for safety or incidents of operational importance, increases the awareness of such scenarios in the community and helps to broaden the overall safety of the NAS, whereas only monitoring the frequency of known events can only provide mitigations for already established problems. This paper discusses a novel approach for discovering operationally significant events in the NAS that are currently not monitored and have potential safety
and/or efficiency implications using radar-track data. This paper will discuss the discovery algorithm and describe in detail some flights of interest with comments from subject matter experts who are familiar with the operations in the airspace that was studied.
not available
not available
not available
Please find a detailed description here: [ACCEPT](http://ti.arc.nasa.gov/opensource/projects/accept/)
Please find a detailed description here: [ROC Curve Code Augmentation](http://ti.arc.nasa.gov/opensource/projects/roc/)
Electro-mechanical actuators (EMAs) have been gaining increased acceptance as safety-critical actuation devices in the next generation of aircraft and spacecraft.  The aerospace manufacturers are not ready, however, to completely embrace EMAs for all applications due to apprehension with regard to some of the more critical fault modes.  This work aims to help address these concerns by developing and testing a prognostic health management system that diagnoses EMA faults and employs prognostic algorithms to track fault progression and predict the actuator remaining useful life. The diagnostic algorithm is implemented using a combined model-based and data-driven reasoner. The prognostic algorithm, implemented using Gaussian Process Regression, estimates the remaining life of the faulted component. The paper also covers the selection of fault modes for coverage and methods developed for fault injection. Validation experiments were conducted both in laboratory and flight conditions using the Flyable Electromechanical Actuator (FLEA) test stand. The FLEA allows test actuators to be subjected to realistic environmental and operating conditions, while providing the capability to safely inject and monitor propagation of various fault modes. The paper covers both diagnostic and prognostic, run-to-failure experiments, conducted in laboratory and flight conditions for several types of faults. The experiments demonstrated robust fault diagnosis on the selected set of component and sensor faults and high-accuracy predictions of failure time in prognostic scenarios.
The zip file linked contains three subdirectories and a parameter list to be used for processing using ACCEPT. Datafiles are partitioned into training, validation, and testing sub-directory structures. A text file contains all parameter identifiers associated with the cold complaint prediction scenario. Please note that the parameter list is a text file containing the following lines:

DC114T - Electrical room temperature
DCRCS103 - Carbon Dioxide concentration sensor in first floor conference room
DCTN240T - Second floor conference room temperature

Please note that Appendix E of an accompanying technical report will be posted here shortly, which will provide more detail on the cold complaint scenario, along with step-by-step directions on how to reproduce the results for this example using ACCEPT. Other appendices will provide detailed set up and configuration instructions.

Here is the citation:

Rodney Martin, Santanu Das, Vijay Janakiraman, Stefan Hosein. ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox, NASA Technical Report, NASA/TM–2015– 218927, November 2015.
Failure prognosis - as a natural extension to the fault detection and isolation (FDI) problem - has become a key issue in a world where the economic impact of system reliability and cost-effective operation of critical assets is steadily increasing. Failure prognostic algorithms aim to characterize the evolution of incipient fault conditions in complex dynamic processes, thus allowing to estimate of the remaining useful life (RUL) of subsystems and components. Several examples can be used here to illustrate the range of possible applications for these algorithms: electro-mechanical systems, continuous-time manufacturing processes, structural damage analysis, and even fault tolerant
software architectures. Most of them have in common the fact that they are highly complex, nonlinear, and affected by large-grain uncertainty.

We introduce in this chapter an integrated failure prognosis architecture that is applicable to a variety of aircraft systems and industrial processes. We are targeting a specific rotorcraft system as a prototypical testbed for proof-of-concept. The overall architecture consists of an on-board and an off-board module for eventual on-platformimplementation purposes.
The chapter describes the application of prognostic techniques to the domain of structural health and
demonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated future usage. The methods shown here use a physics-based modeling approach whereby the behavior of the damaged components is encapsulated via mathematical equations that describe the characteristics of the components as it experiences increasing degrees of degradation. Mathematical rigorous techniques are used to extrapolate the remaining life to a failure threshold. Additionally, mathematical tools are used to calculate the uncertainty associated with making predictions. The information stemming from the predictions can be used in an operational context for go/no go decisions, quantify risk of ability to complete a (set of) mission or operation, and when to schedule maintenance.
This paper presents a reliability-based prediction methodology to obtain the remaining useful life of composite materials subjected to fatigue degradation. Degradation phenomena such as stiffness reduction and increase in matrix micro cracks density are sequentially estimated through a Bayesian filtering framework that incorporates information from both multi-scale damage models and damage measurements, that are sequentially collected along the process. A set of damage states are further propagated forward in time by simulating the damage progression using the models in the absence of new damage measurements to estimate the time-dependent reliability of the composite material. As a key contribution, the estimation of the remaining useful life is obtained as a probability from the prediction of the time-dependent reliability, whose validity is formally proven using the axioms of Probability Logic. A case study is presented using multi-scale fatigue damage data from a cross-ply carbon-epoxy laminate.
In this paper, a real-time composite fatigue life prognosis framework is proposed. The proposed methodology combines Bayesian inference, piezoelectric sensor measurements, and a mechanical stiffness degradation model for in-situ fatigue life prediction. First, the composites stiffness degradation is introduced to account for the composites fatigue damage accumulation under cyclic loadings and a new growth rate-based stiffness degradation model is developed. Following this, the general Bayesian updating-based fatigue life prediction method is discussed. Several sources of uncertainties and the developed stiffness degradation model are included in the prognosis framework. Next, an in-situ composites fatigue testing with piezoelectric sensors is designed and performed to collected sensor signal and the global stiffness data. Signal processing techniques are implemented to extract damage diagnosis features. The detected stiffness degradation is integrated in the Bayesian inference framework for the remaining useful life (RUL) prediction. Prognosis performance on experimental data is validated using prognostics metric. Finally, some conclusions and future work are drawn based on the proposed study.
This paper proposes a recursive receding horizon path planning algorithm for unmanned vehicles
in nonuniform environments. In the proposed algorithm, the map is described by grids in which nodes are defined on corners of grids. The planning algorithm considers the map as four areas, namely, implementation, observation, explored, and unknown. The Implementation area is a subset
of the Observation area, whereas the Explored area is the union of all the previous Observation areas. The path is planned with a receding horizon planning strategy to generate waypoints and in-between map updates. When a new map update occurs, the path is replanned within the current Observation area if necessary. If no such path exists, the search is extended to the Explored area. Paths can be planned by recursively searching available nodes inside the Explored area that can be connected to available nodes on the boundary of the Explored area. A robot platform is employed to conduct a series of experiments in a laboratory environment to verify the proposed path planning algorithm.
Pneumatic-actuated valves are critical components in many applications, including cryogenic propellant loading for space operations. For these components, failures need to be predicted so that components can be repaired to ensure mission success, i.e., health monitoring and fault prognostics is required. In order to develop, test, mature, and deploy valve prognostics algorithms, we have developed a testbed for pneumatic valves used in cryogenic service for propellant loading operations, in which we can inject controlled damage profiles and observe its effects on valve operation. In this paper, we focus on the prognostics of a continuously-controlled pneumatic valve. We describe the construction of the testbed, the fault injection mechanisms, and the model-based valve prognostics algorithms. Experimental results from the testbed demonstrate successful prediction of valve failure.
Prognostics technologies determine the health state of a system and predict its remaining useful life. With this information, operators are able to make maintenance-related decisions, thus effectively streamlining operational and mission level activities. Experimentation on testbeds representative of
critical systems is very useful for the maturation of prognostics technology; precise emulation of actual fault conditions on such a testbed further validates these technologies. In this paper we
present the development of a pneumatic valve testbed, initial experimental results and progress towards the maturation and validation of component-level prognostic methods in the context
of cryogenic refueling operations. The pneumatic valve testbed allows for the injection of time-varying leaks with specified damage progression profiles in order to emulate common valve faults. The pneumatic valve testbed also contains a battery used to power some pneumatic components, enabling the study of the effects of battery degradation on the operation of the valves.
This paper proposes a physics based degradation modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors are critical components in electronics systems in aeronautics and other domains. Degradation's in capacitor and MOSFET components are often the cause of failures in DC-DC converters. For example, prevalent fault effects, such as a ripple voltage surge at the power supply output, can damage interconnected critical subsystems leading to cascading fault propagation. Prognostics in general and in this case electronics components in particular is concerned with the prediction of remaining useful life (RUL) of components and systems. It performs a condition-based health assessment by estimating the current state of health. Furthermore, it leverages the knowledge of the device physics and degradation physics to predict remaining useful life as a function of current state of health and anticipated operational and environmental conditions. Physics-based models capture degradation phenomena in terms of component geometry and energy based principles that define the effect of stressors on the component behavior. This is in contrast to the traditional approach for deriving degradation models from empirical data. Implementing the degradation  modeling techniques
present a general methodology for estimating lifetimes due to specific failure mechanisms. The failure
rate models can be tuned to include parameters that relate to the present health of the device/system and the
expected conditions under which it will be operated. The models and algorithms are applied to data from
degradation experiments of several COTS capacitors. Results show the efficiency of the approach chosen.
Prognostics methodologies determine the health state of a system and predict the end of life and remaining useful life. This information enables operators to take maintenance related decisions, thus effectively streamlining operational and mission-level activities. Prognostics testbeds help in the prototyping, development, and maturation of prognostic technologies. In this work, we present a prognostics testbed for pneumatic valves. Pneumatic valves are critical components in many industrial
processes, and the testbed will be used to showcase how remaining life prediction works in the context of cryogenic refueling operations. The testbed allows for the injection of time-varying leaks
with specified damage progression profiles in order to emulate common valve faults. In addition, the testbed contains a battery used to power some components, allowing the study of the effects of battery degradation on the operation of the valves. Prognostic algorithms will utilize sensor data collected from the different transducers in order to estimate component health and make life predictions, based on mathematical models describing the underlying physics of component degradation and employing a Bayesian filtering algorithm for state-parameter estimation from
which life predictions are made.
Studying and analyzing the ageing mechanisms of electronic components avionics in systems such as the GPS and INAV are of critical importance. In DC-DC power converter systems electrolytic capacitors and MOSFET’s have higher failure rates among the components. Degradation in the capacitors under varying operating conditions leads to high ripples output voltages and currents affecting downstream components leading to cascading faults. For example, in avionics systems where the power supply drives a GPS unit, ripple currents can cause glitches in the GPS position and velocity output, and this may cause errors in the Inertial Navigation (INAV) system, causing the aircraft to fly off course The work in this paper proposes a detail experimental and systematic study on analyzing the degradation phenomenon is electrolytic capacitors under high stress operating conditions. The output degradation is typically measured by an increase in ESR (Equivalent Series Resistance) and decrease in the capacitance value. We present the details of our accelerated ageing methodology along with analysis and comparison of the results.
This paper discusses our initial efforts in constructing physics of failure models for electrolytic capacitors subjected to electrical stressors in DC-DC power converters. Electrolytic capacitors and MOSFET’s are known to be the primary causes for degradation and failure in DC-DC converter systems. We have employed a topological energy based modeling scheme based on the bond graph (BG) modeling language for building parametric models of multi-domain systems, such as motors and pumps. In previous work, we have conducted experimental studies to validate an empirical physics of failure model based on Arrhenius Law for equivalent series resistance (ESR) increase in electrolytic capacitors operating under nominal conditions. In this paper, our focus shifts to deriving first principle models of capacitor degradation that explain both the ESR increase and the decrease in capacitance over time when the capacitor is operated under electrical stress conditions. Experimental studies are run in parallel, and data collected from these studies are used to validate the generated models. In the future, they will also be used to compute model parameters, so that the overall goal of deriving accurate models of capacitor degradation, and using them to predict performance changes in DC-DC converters is realized.
This document outlines NASA's IT management and decision-making structure as well as the the Office of the Chief Information Officer's (OCIO) self-assessment against the Federal Information Technology Reform Act (FITARA) requirements.
This is an API for the Earth Polychromatic Imaging Camera.