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Optimal preventive maintenance and repair policies for multi-state systems

Author

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  • Sheu, Shey-Huei
  • Chang, Chin-Chih
  • Chen, Yen-Luan
  • George Zhang, Zhe

Abstract

This paper studies the optimal preventive maintenance (PM) policies for multi-state systems. The scheduled PMs can be either imperfect or perfect type. The improved effective age is utilized to model the effect of an imperfect PM. The system is considered as in a failure state (unacceptable state) once its performance level falls below a given customer demand level. If the system fails before a scheduled PM, it is repaired and becomes operational again. We consider three types of major, minimal, and imperfect repair actions, respectively. The deterioration of the system is assumed to follow a non-homogeneous continuous time Markov process (NHCTMP) with finite state space. A recursive approach is proposed to efficiently compute the time-dependent distribution of the multi-state system. For each repair type, we find the optimal PM schedule that minimizes the average cost rate. The main implication of our results is that in determining the optimal scheduled PM, choosing the right repair type will significantly improve the efficiency of the system maintenance. Thus PM and repair decisions must be made jointly to achieve the best performance.

Suggested Citation

  • Sheu, Shey-Huei & Chang, Chin-Chih & Chen, Yen-Luan & George Zhang, Zhe, 2015. "Optimal preventive maintenance and repair policies for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 78-87.
    • Handle: RePEc:eee:reensy:v:140:y:2015:i:c:p:78-87
    DOI: 10.1016/j.ress.2015.03.029
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    References listed on IDEAS

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    Cited by:

    1. Koutras, V.P. & Malefaki, S. & Platis, A.N., 2017. "Optimization of the dependability and performance measures of a generic model for multi-state deteriorating systems under maintenance," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 73-86.
    2. Zhao, Xufeng & Qian, Cunhua & Nakagawa, Toshio, 2017. "Comparisons of replacement policies with periodic times and repair numbers," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 161-170.
    3. Tsai, Hsin-Nan & Sheu, Shey-Huei & Zhang, Zhe George, 2017. "A trivariate optimal replacement policy for a deteriorating system based on cumulative damage and inspections," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 74-88.
    4. Xiaosheng Zhang & Jianqiao Chen & Ben Han & Junxiang Li, 2019. "Multi-mission selective maintenance modelling for multistate systems over a finite time horizon," Journal of Risk and Reliability, , vol. 233(6), pages 1040-1059, December.
    5. Guo, Chunhui & Liang, Zhenglin, 2022. "A predictive Markov decision process for optimizing inspection and maintenance strategies of partially observable multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    6. Tsai, Hsin-Nan & Sheu, Shey-Huei & Zhang, Zhe George, 2017. "A trivariate optimal replacement policy for a deteriorating system based on cumulative damage and inspections," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 122-135.
    7. Zhang, Aibo & Srivastav, Himanshu & Barros, Anne & Liu, Yiliu, 2021. "Study of testing and maintenance strategies for redundant final elements in SIS with imperfect detection of degraded state," Reliability Engineering and System Safety, Elsevier, vol. 209(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.
    9. Florian, Eleonora & Sgarbossa, Fabio & Zennaro, Ilenia, 2021. "Machine learning-based predictive maintenance: A cost-oriented model for implementation," International Journal of Production Economics, Elsevier, vol. 236(C).
    10. Vališ, David & Žák, Libor & Pokora, Ondřej & Lánský, Petr, 2016. "Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 231-242.
    11. Constâncio António Pinto & José Torres Farinha & Hugo Raposo & Diego Galar, 2022. "Stochastic versus Fuzzy Models—A Discussion Centered on the Reliability of an Electrical Power Supply System in a Large European Hospital," Energies, MDPI, vol. 15(3), pages 1-21, January.
    12. Memarzadeh, Milad & Pozzi, Matteo & Kolter, J. Zico, 2016. "Hierarchical modeling of systems with similar components: A framework for adaptive monitoring and control," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 159-169.

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