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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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    Cited by:

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    5. Pliego Marugán, Alberto & Pinar-Pérez, Jesús M. & García Márquez, Fausto Pedro, 2024. "A reinforcement learning agent for maintenance of deteriorating systems with increasingly imperfect repairs," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    6. 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).
    7. 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).
    8. Patriarca, Riccardo & Lovaglio, Lorenzo & Simone, Francesco, 2025. "Functional resonance analysis via a genetic algorithm to ensure cost-effective maintenance planning," International Journal of Production Economics, Elsevier, vol. 281(C).
    9. 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).
    10. 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).
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