Condition-Based Maintenance Modeling and Reliability Assessment for Multi-Component Systems with Structural Dependence under Extended Warranty

Author:

Wang Rongcai1ORCID,Cheng Zhonghua1ORCID,Dong Enzhi1,Rong Liqing1

Affiliation:

1. Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China

Abstract

For engineering and production systems, due to the structural dependence between components, the disassembly operation caused by the replacement of components will affect the failure and degradation processes of other components in the system. In order to optimize the extended warranty (EW) cost of the multi‐component system with structural dependence, this paper described the structural dependence and modeled the disassembly operation impact, and then the failure rate model of the component considering the impact of disassembly operation under EW was developed. Combined with the actual situation, a condition-based maintenance (CBM) strategy was employed to construct the EW cost model of the multi‐component system with structural dependence. Monte Carlo simulation was proposed to determine the optimal EW cost of the system and the optimal periodic inspection interval of the CBM strategy. Finally, a numerical example of the planetary gear train of an automobile generator is introduced to demonstrate the feasibility and advantages of the proposed model in EW cost optimization and the analysis of disassembly operation impact on the optimal maintenance strategy.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimal Periodic Inspection for Multi-Auxiliary Component Systems Subject to Load-Sharing Dependence;2022 4th International Conference on System Reliability and Safety Engineering (SRSE);2022-12-15

2. Reliability Evaluation and Maintenance Planning for Systems With Load-Sharing Auxiliary Components;IEEE Transactions on Reliability;2022

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