HSMM multi-observations for prognostics and health management

Lestari Handayani, Pascal Vrignat and Frédéric Kratz

Journal of Risk and Reliability, 2025, vol. 239, issue 2, 253-275

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.

Keywords: Prognostics and health management; Hidden Markov Model; Hidden Semi-Markov Model; remaining useful life (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:239:y:2025:i:2:p:253-275

DOI: 10.1177/1748006X241238582

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