Journal of Shanghai Jiao Tong University ›› 2021, Vol. 55 ›› Issue (7): 858-867.doi: 10.16183/j.cnki.jsjtu.2019.221

Special Issue: 《上海交通大学学报》2021年12期专题汇总专辑 《上海交通大学学报》2021年“工业工程与管理”专题

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Optimization Model of Maintenance and Spare Parts Ordering Policy in Multivariate Degradation System

YANG Zhiyuan1, ZHAO Jianmin1, CHENG Zhonghua1(), GUO Chiming1, LI Liying2   

  1. 1. Department of Equipment Command and Management, Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China
    2. School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China
  • Received:2019-07-29 Online:2021-07-28 Published:2021-07-30
  • Contact: CHENG Zhonghua


Aimed at the decision-making problem of condition-based maintenance and spare parts ordering for systems with multiple dependent degradation processes, an optimization model of system maintenance and spare parts ordering policy is developed under the condition of continuously monitoring. First, the Gamma process and Copula function are used to develop the system multivariate degradation model. Then, the system maintenance and spare parts ordering policy based on the control limit strategy is proposed. Considering the influence of system degradation on maintenance cost, the analytical expression of the expected maintenance cost rate under long-term operation conditions is obtained. At the same time, an approximate expression of the expected maintenance cost rate is proposed to simplify the model calculation. The optimal preventive replacement threshold and spare parts ordering threshold of the system are obtained by using the artificial bee colony algorithm under the cost criterion. The case analysis shows that it is necessary to consider degradation in maintenance decision-making. Compared with the existing policy, the comprehensive optimization of preventive replacement and spare parts ordering thresholds can effectively reduce the maintenance cost of the system.

Key words: maintenance decision, spare parts ordering, multivariate degradation process, dependent degradation, condition monitoring

CLC Number: