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Maintenance optimization for dependent two-component degrading systems subject to imperfect repair

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  • Cheng, Wanqing
  • Zhao, Xiujie

Abstract

Appropriate maintenance policies play an important role in improving system availability and ensuring safe operation. Seeking optimal maintenance policies for technical systems has been widely pursued by reliability engineers and researchers. In this paper, we propose a maintenance optimization method that is applicable to dependent two-component systems subject to degradation and imperfect repair. We consider both economic and stochastic dependencies between the components and establish a random-effect imperfect repair model to realistically model the degradation process and maintainability of components. Moreover, we model the maintenance problem under the infinite horizon using the Markov decision process and obtain the optimal solution via value iteration algorithm. Structural insights are gleaned using the stochastic orders. A numerical example is then presented to illustrate the proposed methods. We discover that the characteristics of imperfect repair can considerably influence the optimal policies. Specifically, the mean effect of imperfect repair has a larger influence on maintenance decisions while the influence of imperfect repair variability effect is relatively small.

Suggested Citation

  • Cheng, Wanqing & Zhao, Xiujie, 2023. "Maintenance optimization for dependent two-component degrading systems subject to imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023004957
    DOI: 10.1016/j.ress.2023.109581
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    References listed on IDEAS

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    1. Akash Deep & Shiyu Zhou & Dharmaraj Veeramani, 2022. "A data-driven recurrent event model for system degradation with imperfect maintenance actions," IISE Transactions, Taylor & Francis Journals, vol. 54(3), pages 271-285, March.
    2. MERCIER, Sophie & CASTRO, I.T., 2019. "Stochastic comparisons of imperfect maintenance models for a gamma deteriorating system," European Journal of Operational Research, Elsevier, vol. 273(1), pages 237-248.
    3. Do, Phuc & Assaf, Roy & Scarf, Phil & Iung, Benoit, 2019. "Modelling and application of condition-based maintenance for a two-component system with stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 86-97.
    4. Mosayebi Omshi, E. & Grall, A., 2021. "Replacement and imperfect repair of deteriorating system: Study of a CBM policy and impact of repair efficiency," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Shahraki, Ameneh Forouzandeh & Yadav, Om Prakash & Vogiatzis, Chrysafis, 2020. "Selective maintenance optimization for multi-state systems considering stochastically dependent components and stochastic imperfect maintenance actions," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    6. Wu, Shaomin, 2019. "A failure process model with the exponential smoothing of intensity functions," European Journal of Operational Research, Elsevier, vol. 275(2), pages 502-513.
    7. Liu, Bin & Pandey, Mahesh D. & Wang, Xiaolin & Zhao, Xiujie, 2021. "A finite-horizon condition-based maintenance policy for a two-unit system with dependent degradation processes," European Journal of Operational Research, Elsevier, vol. 295(2), pages 705-717.
    8. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    9. Liu, Yu & Chen, Yiming & Jiang, Tao, 2020. "Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach," European Journal of Operational Research, Elsevier, vol. 283(1), pages 166-181.
    10. Liu, Gehui & Chen, Shaokuan & Jin, Hua & Liu, Shuang, 2021. "Optimum opportunistic maintenance schedule incorporating delay time theory with imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    11. Yang, Hongbing & Li, Wenchao & Wang, Bin, 2021. "Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    12. Zhao, Xiujie & Chen, Piao & Gaudoin, Olivier & Doyen, Laurent, 2021. "Accelerated degradation tests with inspection effects," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1099-1114.
    13. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    14. Xu, Jun & Liang, Zhenglin & Li, Yan-Fu & Wang, Kaibo, 2021. "Generalized condition-based maintenance optimization for multi-component systems considering stochastic dependency and imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    15. Fang, Guanqi & Pan, Rong & Hong, Yili, 2020. "Copula-based reliability analysis of degrading systems with dependent failures," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    16. Andriotis, C.P. & Papakonstantinou, K.G., 2021. "Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
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    Cited by:

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    5. Cousino, Théo & Brissaud, Florent & Doyen, Laurent & Gaudoin, Olivier & Marle, Leïla, 2024. "Imperfect maintenance modelling and estimation for interval-censored data," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    6. Zhang, Wenyu & He, Shuguang & Zhang, Xiaohong & Zhao, Xing, 2024. "Joint optimization of job scheduling, condition-based maintenance planning, and spare parts ordering for degrading production systems," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    7. Goyal, Dheeraj & Finkelstein, Maxim & Hazra, Nil Kamal, 2025. "On repairable systems with time redundancy and operational constraints," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    8. Liang, Xiaojun & Cui, Lirong & Wang, Ruiting, 2024. "Non-renewable warranty cost analysis for dependent series configuration with distinct warranty periods," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    9. Ning, Ru & Wang, Xiaoyue & Zhao, Xian & Li, Ziyue, 2024. "Joint optimization of preventive maintenance and triggering mechanism for k-out-of-n: F systems with protective devices based on periodic inspection," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
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    11. Beal, John & Reihani, Seyed & Sakurahara, Tatsuya & Kee, Ernie & Mohaghegh, Zahra, 2025. "Modeling nuclear power plant piping reliability by coupling a human reliability analysis-based maintenance model with a physical degradation model," Reliability Engineering and System Safety, Elsevier, vol. 255(C).

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