Joint optimization of condition-based maintenance and spares inventory for a series-parallel system with two failure modes

备品备件 预防性维护 可靠性工程 纠正性维护 计算机科学 状态维修 过程(计算) 马尔可夫决策过程 马尔可夫链 最佳维护 系列(地层学) 马尔可夫过程 接头(建筑物) 运筹学 工程类
作者
Jing Zhang,Xian Zhao,Yanbo Song,Qingan Qiu
出处
期刊:Computers & Industrial Engineering [Elsevier]
卷期号:: 108094-108094
标识
DOI:10.1016/j.cie.2022.108094
摘要

• A joint model of condition-based maintenance and spares inventory is constructed. • Hard failures and soft failures are creatively considered simultaneously. • A mixed inspection policy is adopted firstly. • A semi-Markov decision process is formulated to derive the related indices. • The threshold of preventive maintenance and inventory policy are optimized. In the field of industrial engineering, the joint optimization of maintenance and spares inventory has attracted more and more attention because it can better balance system availability and cost. However existing studies are usually restricted to a single failure mode and a simple maintenance strategy of the single component systems. To bridge these gaps, this paper investigates the joint optimization of condition-based maintenance and spares inventory for a general series–parallel system with two failure modes. Hard failures are self-announcing, and soft failures are generally caused by the degradation of components and only be discovered through inspection. At the time of each periodic inspection, the corresponding corrective maintenance, preventive maintenance and spare parts ordering policy are determined. Upon a hard failure occurs, an opportunistic inspection will be performed, and it will be determined whether to perform maintenance actions based on the degradation level of the components and spares inventory level. Furthermore, the state transition probability and the expected sojourn time can be derived by the formulated semi-Markov decision process. To minimize the expected average cost per unit time, the optimal preventive maintenance and the spares inventory control policies are jointly determined by applying a simulation method. Finally, a numerical experiment is presented to demonstrate the effectiveness and superiority of the proposed joint optimization model.
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