A new preventive maintenance strategy optimization model considering lifecycle safety

•A general PMSO model that can consider the lifecycle safety of structure is proposed.•The effect of maintenance on performance function is explored in the established model.•Only one surrogate model is constructed to estimate failure probabilities of different functions.•A two-level surrogate model...

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Veröffentlicht in:Reliability engineering & system safety 2022-05, Vol.221, p.108325, Article 108325
Hauptverfasser: Shi, Yan, Lu, Zhenzhou, Huang, Hongzhong, Liu, Yu, Li, Yanfeng, Zio, Enrico, Zhou, Yicheng
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Sprache:eng
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Zusammenfassung:•A general PMSO model that can consider the lifecycle safety of structure is proposed.•The effect of maintenance on performance function is explored in the established model.•Only one surrogate model is constructed to estimate failure probabilities of different functions.•A two-level surrogate model is established to identify the optimal maintenance time. 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.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2022.108325