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HSMM multi-observations for prognostics and health management

Author

Listed:
  • Lestari Handayani
  • Pascal Vrignat
  • Frédéric Kratz

Abstract

An efficient maintenance policy allows for determining the current state of a system (diagnosis phase) and its future state (prognosis phase). We show in this paper that Markovian methods allow for obtaining many efficient indicators for the expert. To characterize the quality and robustness of these methods, we compared the Hidden Semi-Markov Model (HSMM) with the Hidden Markov Model (HMM). Several learning and decoding methods were included in the competition. A real case study was used as a particularly interesting working tool. The Remaining Useful Life (RUL) has also been included in this work.

Suggested Citation

  • Lestari Handayani & Pascal Vrignat & Frédéric Kratz, 2025. "HSMM multi-observations for prognostics and health management," Journal of Risk and Reliability, , vol. 239(2), pages 253-275, April.
    • Handle: RePEc:sae:risrel:v:239:y:2025:i:2:p:253-275
    DOI: 10.1177/1748006X241238582
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    References listed on IDEAS

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    6. Di Zhou & Hongtao Yin & Ping Fu & Xianhua Song & Wenbin Lu & Lili Yuan & Zuoxian Fu, 2018. "Prognostics for State of Health of Lithium-Ion Batteries Based on Gaussian Process Regression," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, April.
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