Optimal Inspection and Replacement Policies for Multi-Unit Systems Subject to Degradation

• Published in: IEEE transactions on reliability Vol. 67; no. 1; pp. 401 - 413
• Main Authors: Sun, Qiuzhuang, Ye, Zhi-Sheng, Chen, Nan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-03-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Black boxes; Condition monitoring; Condition-based maintenance (CBM); control limit policy; Control limits; Degradation; Inspection; Iterative algorithms; Iterative methods; K-out-of-N: G systems; Maintenance costs; Maintenance engineering; Maintenance management; Markov decision process; Markov processes; Optimal control; Process control; Sensitivity analysis; Strategy; System effectiveness; Wiener process
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2017.2778283

Condition-based maintenance (CBM) is proved to be effective in reducing the long-run operational cost for a system subject to degradation failure. Most existing research on CBM focuses on single-unit systems where the whole system is treated as a black box. However, a system usually consists of a number of components and each component has its failure behavior. When degradation of the components is observable, CBM can be applied to the component level to improve the maintenance efficiency. This paper aims to study the optimal inspection/replacement CBM strategy for a multi-unit system. Degradation of each component is assumed to follow a Wiener process and periodic inspection is considered. We cast the problem into a Markov decision framework and derive the optimal maintenance decisions that minimize the maintenance cost. To better illustrate the optimal maintenance strategy, we start from a 1-out-of-2: G system and show that the optimal maintenance policy is a two-dimensional control limit policy. The argument used in the 1-out-of-2: G system can be readily extended to general cases in a similar way. The value iteration algorithm is used to find the optimal control limits, and the optimal inspection interval is subsequently determined through a one-dimensional search. A numerical study and a comprehensive sensitivity analysis are provided to illustrate the optimal maintenance strategy.