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An optimal opportunistic maintenance policy for a two-unit series system with general repair using proportional hazards models

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  • Najafi, Seyedvahid
  • Zheng, Rui
  • Lee, Chi-Guhn

Abstract

This paper proposes an opportunistic maintenance policy for maintaining mechanical systems consisting of two series units with economic dependency. Condition monitoring and age information is available for unit 1 and unit 2, respectively. A gamma process describes the stochastic deterioration of unit 1, and condition monitoring data is utilized to estimate its hazard rate using the proportional hazards model (PHM). A matrix-based algorithm is employed to derive the conditional reliability of a two-unit series system with a continuous-state deterioration process. The policy proposes preventive actions on scheduled intervals, based on lifetime and PHM parameters, to enhance system reliability and mitigate unexpected downtimes. Corrective actions rectify the failed unit, while the other unit can be maintained opportunistically. The problem is formulated as a semi-Markov decision process (SMDP) with an objective of minimizing the long-run expected average cost over an infinite time horizon. This paper relaxes a common assumption that repair brings the units to the as-new condition, and provides solutions for the maintenance scheduling of two-unit series systems, where repair is also allowed.

Suggested Citation

  • Najafi, Seyedvahid & Zheng, Rui & Lee, Chi-Guhn, 2021. "An optimal opportunistic maintenance policy for a two-unit series system with general repair using proportional hazards models," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003513
    DOI: 10.1016/j.ress.2021.107830
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    References listed on IDEAS

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    3. Louzada, Francisco & Tomazella, Vera L.D. & Gonzatto, Oilson A. & Bochio, Gustavo & Milani, Eder A. & Ferreira, Paulo H. & Ramos, Pedro L., 2022. "Reliability assessment of repairable systems with series–parallel structure subjected to hierarchical competing risks under minimal repair regime," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Najafi, Seyedvahid & Lee, Chi-Guhn, 2023. "A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Wu, Jing & Qian, Cunhua & Dohi, Tadashi, 2024. "Optimal opportunity-based age replacement policies in discrete time," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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    7. Azizi, Fariba & Salari, Nooshin, 2023. "A novel condition-based maintenance framework for parallel manufacturing systems based on bivariate birth/birth–death processes," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    8. Li, Yaping & Xia, Tangbin & Chen, Zhen & Pan, Ershun, 2023. "Multiple degradation-driven preventive maintenance policy for serial-parallel multi-station manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Xia, Tangbin & Cao, Lei & Xu, Yuhui & Zhang, Kaigan & Chen, Zhen & Pan, Ershun & Xi, Lifeng, 2024. "Multi-level maintenance and inventory joint optimization for a k-out-of-n hyper-system considering the selection of suppliers with incentive discount policies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Zhou, Yifan & Li, Bangcheng & Lin, Tian Ran, 2022. "Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    11. Pedersen, Tom Ivar & Liu, Xingheng & Vatn, Jørn, 2023. "Maintenance optimization of a system subject to two-stage degradation, hard failure, and imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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