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Hidden Semi-Markov Models for Predictive Maintenance

Author

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  • Francesco Cartella
  • Jan Lemeire
  • Luca Dimiccoli
  • Hichem Sahli

Abstract

Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.

Suggested Citation

  • Francesco Cartella & Jan Lemeire & Luca Dimiccoli & Hichem Sahli, 2015. "Hidden Semi-Markov Models for Predictive Maintenance," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-23, February.
  • Handle: RePEc:hin:jnlmpe:278120
    DOI: 10.1155/2015/278120
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    Cited by:

    1. Deep, Akash & Zhou, Shiyu & Veeramani, Dharmaraj & Chen, Yong, 2023. "Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations," European Journal of Operational Research, Elsevier, vol. 311(2), pages 533-544.
    2. Morteza Amini & Afarin Bayat & Reza Salehian, 2023. "hhsmm: an R package for hidden hybrid Markov/semi-Markov models," Computational Statistics, Springer, vol. 38(3), pages 1283-1335, September.

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