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Hidden markov models in reliability and maintenance

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  • Gámiz, María Luz
  • Limnios, Nikolaos
  • Segovia-García, María del Carmen

Abstract

Although the hidden Markov models (HMM) are very popular in many applied areas their use in reliability engineering is limited. Problems such as the selection of the HMM model by choosing the appropriate number of states, or problems of prediction of failures have not been widely covered in the literature. This paper is concerned with the use of HMMs where the state of the system is not directly observable and instead certain indicators of the true situation are provided via a control system. A hidden model can provide key information about the system dependability such as the failed component of the system, the reliability of the system and related measures. A maximum-likelihood estimator of the system reliability is obtained and its asymptotic properties are studied. Finally, the maintenance of the system is considered in this context and new preventive maintenance strategies are defined and their efficiency is measured in terms of expected cost. To prove the finite sample performance of the methodology, an extensive simulation study is developed.

Suggested Citation

  • Gámiz, María Luz & Limnios, Nikolaos & Segovia-García, María del Carmen, 2023. "Hidden markov models in reliability and maintenance," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1242-1255.
    • Handle: RePEc:eee:ejores:v:304:y:2023:i:3:p:1242-1255
    DOI: 10.1016/j.ejor.2022.05.006
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Jingqi & Fouladirad, Mitra & Limnios, Nikolaos, 2025. "Sensitivity analysis of an imperfect maintenance policy for Proton-exchange membrane fuel cell based on geometric a semi-Markov model," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    2. Aven, Terje & Rios Insua, David & Soyer, Refik & Zhu, Xiaoyan & Zio, Enrico, 2025. "Fifty years of reliability in operations research," European Journal of Operational Research, Elsevier, vol. 324(2), pages 361-381.
    3. María Luz Gámiz & Nikolaos Limnios & Mari Carmen Segovia-García, 2023. "The continuous-time hidden Markov model based on discretization. Properties of estimators and applications," Statistical Inference for Stochastic Processes, Springer, vol. 26(3), pages 525-550, October.
    4. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    5. Gámiz, M.L. & Navas-Gómez, F. & Raya-Miranda, R. & Segovia-García, M.C., 2023. "Dynamic reliability and sensitivity analysis based on HMM models with Markovian signal process," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    6. Arts, Joachim & Boute, Robert N. & Loeys, Stijn & van Staden, Heletjé E., 2025. "Fifty years of maintenance optimization: Reflections and perspectives," European Journal of Operational Research, Elsevier, vol. 322(3), pages 725-739.
    7. Xu, Jianyu & Liu, Bin & Zhao, Xiujie & Wang, Xiao-Lin, 2024. "Online reinforcement learning for condition-based group maintenance using factored Markov decision processes," European Journal of Operational Research, Elsevier, vol. 315(1), pages 176-190.
    8. Claire Y. T. Chen & Edward W. Sun & Yi-Bing Lin, 2025. "Reliable information system for identifying spatio-temporal continuity of kinetic deformed objects with big point cloud data," Annals of Operations Research, Springer, vol. 349(1), pages 103-138, June.

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