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Prognosis of Dynamical System Components with Varying Degradation Patterns using model–data–fusion

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  • Prakash, Om
  • Samantaray, Arun Kumar

Abstract

Failure prognosis is used to predict the future degradation and remaining useful life (RUL) of components. However, identification of future degradation and RUL of components is challenging when similar components in the same or different working conditions show varying degradation patterns. This is again more challenging when the component shows highly nonlinear degradation behavior, which may lead to erroneous RUL prediction and wrong prognosis. In this paper, a hybrid approach with the fusion of model–based and data–driven approaches is developed for the prognosis of dynamical system components whose degradations may follow different nonlinear trends. Here, degradation levels of different components are identified by using bond graph model–based distributed prognosis approach. However, artificial neural network based degradation models learned from the run–to–failure data of the components are used to predict the future degradation patterns and RUL of the components. The developed hybrid approach is applied to an electronic circuit test bed.

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  • Prakash, Om & Samantaray, Arun Kumar, 2021. "Prognosis of Dynamical System Components with Varying Degradation Patterns using model–data–fusion," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021002210
    DOI: 10.1016/j.ress.2021.107683
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    Cited by:

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    2. Balyogi Mohan Dash & Om Prakash & Arun Kumar Samantaray, 2023. "Failure prognosis of the components with unlike degradation trends: A data-driven approach," Journal of Risk and Reliability, , vol. 237(6), pages 1132-1149, December.
    3. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
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    6. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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