Dynamic strain mapping and real-time damage state estimation under biaxial random fatigue loading

Subhasish Mohanty, Aditi Chattopadhyay, John Rajadas, Clyde Coelho

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 Conference on Intelligent Data Understanding, CIDU 2010
Number of pages14
StatePublished - 2010
EventNASA Conference on Intelligent Data Understanding, CIDU 2010 - Mountain View, CA, United States
Duration: Oct 5 2010Oct 6 2010


OtherNASA Conference on Intelligent Data Understanding, CIDU 2010
Country/TerritoryUnited States
CityMountain View, CA

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software


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