DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE
ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING

SUBHASISH MOHANTY*, ADITI CHATTOPADHYAY*, JOHN N. RAJADAS**, AND CLYDE COELHO*

Abstract. Fatigue damage and its prediction is one of the foremost concerns of structural integrity
research community. The current research in structural health monitoring (SHM) is to
provide continuous (or on demand) information about the state of a structure. The SHM system
can be based on either active or passive sensor measurements. Though the current research on
ultrasonic wave propagation based active sensing approach has the potential to estimate very small
damage, it has severe drawbacks in terms of low sensing radius and external power requirements.
To alleviate these disadvantages passive sensing based SHM techniques can be used. Currently,
few efforts have been made towards, time-series fatigue damage state estimation over the entire
fatigue life (stage-I, II & III). A majority of the available literature on passive sensing SHM techniques
demonstrates the clear trend in damage growth during the final failure regime (stage-III
regime) or during when the damage is comparatively large enough. The present paper proposes a
passive sensing technique that demonstrates a clear trend in damage growth almost over the entire
stage-II and III damage growth regime. A strain gauge measurement based passive SHM frameworks
that can estimate the time-series fatigue damage state under random loading is proposed.
For this purpose, a Bayesian Gaussian process nonlinear dynamic model is developed to map the
reference condition dynamic strain at a given instant of time. The predicted strains are compared
with the actual sensor measurements to estimate the corresponding error signals. The error signals
estimated at two different locations are correlated to estimate the corresponding fatigue damage
state. The approach is demonstrated for an Al-2434 complex cruciform structure applied with
biaxial random loading.