Maintenance Scheduling for Multicomponent Systems with Hidden Failures

Abstract

This paper develops a maintenance policy for a multi-component system subject to hidden failures. Components of the system are assumed to suffer from hidden failures, which can only be detected at inspection. The objective of the maintenance policy is to determine the inspection intervals for each component such that the long-run cost rate is minimized. Due to the dependence among components, an exact optimal solution is difficult to obtain. Concerned with the intractability of the problem, a heuristic method named "base interval approach" is adopted to reduce the computational complexity. Performance of the base interval approach is analyzed and the result shows that the proposed policy can approximate the optimal policy within a small factor. Two numerical examples are presented to illustrate the effectiveness of the policy.

A review on maintenance optimization

“…Other studies consider economic dependence in the sense that a fixed inspection cost is only incurred once regardless of the number of units inspected. Liu, Yeh, Xie & Kuo (2017c) determine heuristic policies for a system consisting of non-identical components. Each component is inspected periodically to detect failures.…”

Stochastic Filtering Approach for Condition-Based Maintenance Considering Sensor Degradation

“…A renewal cycle is defined as the time interval between two consecutive replacements or the time period to the first replacement since system installation. The classical renewal-reward theorem can be applied to calculate the long run maintenance cost rate of (7) [26], [42], which is given as…”

Integrated design of monitoring, analysis and maintenance for filamentous sludge bulking in wastewater treatment

“…Networks, and so on) have been developed, giving rise to the chain behaviors exploration (Borgonovo & Plischke, 2016;Pianosi, et al, 2016). Another solution is to take the algorithm chain as a serial system for reliability analysis (Feng, et al, 2019;B. Liu, Yeh, Xie, & Kuo, 2017).…”