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Article Dans Une Revue Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability Année : 2024

HSMM multi-observations for prognostics and health management

Résumé

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.
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Dates et versions

hal-04536525 , version 1 (08-04-2024)

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Lestari Handayani, Pascal Vrignat, Frédéric Kratz. HSMM multi-observations for prognostics and health management. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2024, ⟨10.1177/1748006X241238582⟩. ⟨hal-04536525⟩
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