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A new preventive maintenance strategy optimization model considering lifecycle safety

Author

Listed:
  • Shi, Yan
  • Lu, Zhenzhou
  • Huang, Hongzhong
  • Liu, Yu
  • Li, Yanfeng
  • Zio, Enrico
  • Zhou, Yicheng

Abstract

Preventive maintenance can improve the structure reliability at the same time balance the cost, thus it has gained widespread concern during the past decades. This work focuses on establishing a general preventive maintenance strategy optimization (PMSO) model for structure by deeply exploring the effect of maintenance on structure performance function, with which the reliability is estimated instead of directly assuming a reliability function for structure. At the same time, the lifecycle safety of structure under maintenance is employed to identify the maintenance strategy since it can provide the solution by considering the different operation time intervals as a whole so that people can fully grasp the maintenance effect. This model is established by decomposing the lifecycle failure state as different failure states or conditional failure states during different operation time intervals, and lifecycle failure probability is finally described by the joint time-dependent failure probability of different operation time intervals after further derivation. Furthermore, an advanced estimation strategy is proposed, in which only one surrogate model is construct and it can accurately estimate the failure probabilities of different performance functions. Then, a two-level surrogate model is further constructed to deal with the difficulties of optimization and stochastic simulation variability in identifying the optimal maintenance time. Several engineering applications are employed to show the effectiveness of the established PMSO model and strategy.

Suggested Citation

  • Shi, Yan & Lu, Zhenzhou & Huang, Hongzhong & Liu, Yu & Li, Yanfeng & Zio, Enrico & Zhou, Yicheng, 2022. "A new preventive maintenance strategy optimization model considering lifecycle safety," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000072
    DOI: 10.1016/j.ress.2022.108325
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    References listed on IDEAS

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    Cited by:

    1. Zhu, Mixin & Zhou, Xiaojun, 2024. "Maintenance modeling of serial-parallel multi-station manufacturing system with failure-induced damage and assembly parts," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    2. Asadi, Majid, 2023. "On a parametric model for the mean number of system repairs with applications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Zheng, Jianfei & Ren, Jincheng & Pei, Hong & Zhang, Jianxun & Zhang, Zhengxin, 2024. "Lifetime prediction and replacement optimization for a standby system considering storage failures of spare parts," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    4. Carlon, André Gustavo & Kroetz, Henrique Machado & Torii, André Jacomel & Lopez, Rafael Holdorf & Miguel, Leandro Fleck Fadel, 2022. "Risk optimization using the Chernoff bound and stochastic gradient descent," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Kim, Seokgoo & Choi, Joo-Ho & Kim, Nam Ho, 2022. "Inspection schedule for prognostics with uncertainty management," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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