For the purposes of this paper, the National
Airspace System (NAS) encompasses the operations of all
aircraft which are subject to air traffic control procedures.
The NAS is a highly complex dynamic system that is
sensitive to aeronautical decision-making and risk management
skills. In order to ensure a healthy system with safe
flights a systematic approach to anomaly detection is very
important when evaluating a given set of circumstances
and for determination of the best possible course of action.
Given the fact that the NAS is a vast and loosely integrated
network of systems, it requires improved safety assurance
capabilities to maintain an extremely low accident rate
under increasingly dense operating conditions. Data mining
based tools and techniques are required to support and aid
operators’ (such as pilots, management, or policy makers)
overall decision-making capacity. Within the NAS, the
ability to analyze fleetwide aircraft data autonomously is
still considered a significantly challenging task. For our
purposes a fleet is defined as a group of aircraft sharing
generally compatible parameter lists. Here, in this effort,
we aim at developing a system level analysis scheme. In this
paper we address the capability for detection of fleetwide
anomalies as they occur, which itself is an important
initiative toward the safety of the real-world flight operations.
The flight data recorders archive millions of data
points with valuable information on flights everyday. The
operational parameters consist of both continuous and discrete
(binary & categorical) data from several critical subsystems
and numerous complex procedures. In this paper,
we discuss a system level anomaly detection approach based
on the theory of kernel learning to detect potential safety
anomalies in a very large data base of commercial aircraft.
We also demonstrate that the proposed approach uncovers
some operationally significant events due to environmental,
mechanical, and human factors issues in high dimensional,
multivariate Flight Operations Quality Assurance (FOQA)
data. We present the results of our detection algorithms on
real FOQA data from a regional carrier.