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Bayesian failure-rate modeling and preventive maintenance optimization

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  • Belyi, Dmitriy
  • Popova, Elmira
  • Morton, David P.
  • Damien, Paul

Abstract

New results are derived for the optimal preventive maintenance schedule of a single item over a finite horizon, based on Bayesian models of a failure rate function. Two types of failure rate functions—increasing and bathtub shapes—are considered. For both cases, optimality conditions and efficient algorithms to find an optimal maintenance schedule are given. A Bayesian parametric model for bathtub-shaped failure rate functions is used, while the class of increasing failure rate functions are tackled by an extended gamma process. We illustrate both approaches using real failure time data from the South Texas Project Nuclear Operating Company in Bay City, Texas.

Suggested Citation

  • Belyi, Dmitriy & Popova, Elmira & Morton, David P. & Damien, Paul, 2017. "Bayesian failure-rate modeling and preventive maintenance optimization," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1085-1093.
    • Handle: RePEc:eee:ejores:v:262:y:2017:i:3:p:1085-1093
    DOI: 10.1016/j.ejor.2017.04.019
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    References listed on IDEAS

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    Cited by:

    1. Antonio Pievatolo & Fabrizio Ruggeri & Refik Soyer & Simon Wilson, 2021. "Decisions in Risk and Reliability: An Explanatory Perspective," Stats, MDPI, vol. 4(2), pages 1-23, March.
    2. Zhao, Yunfei & Smidts, Carol, 2022. "Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Milton Fonseca Junior & Ubiratan Holanda Bezerra & Jandecy Cabral Leite & Jorge Laureano Moya Rodríguez, 2017. "Maintenance Tools applied to Electric Generators to Improve Energy Efficiency and Power Quality of Thermoelectric Power Plants," Energies, MDPI, vol. 10(8), pages 1-21, July.
    4. Dursun, İpek & Akçay, Alp & van Houtum, Geert-Jan, 2022. "Age-based maintenance under population heterogeneity: Optimal exploration and exploitation," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1007-1020.
    5. Hongming Zhou & Sufen Wang & Faqun Qi & Shun Gao, 2022. "Maintenance modeling and operation parameters optimization for complex production line under reliability constraints," Annals of Operations Research, Springer, vol. 311(1), pages 507-523, April.
    6. Insua, David Rios & Ruggeri, Fabrizio & Soyer, Refik & Wilson, Simon, 2020. "Advances in Bayesian decision making in reliability," European Journal of Operational Research, Elsevier, vol. 282(1), pages 1-18.
    7. Zhao, Yunfei & Gao, Wei & Smidts, Carol, 2021. "Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.

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