Long-term predictive opportunistic replacement optimisation for a small multi-component system using partial condition monitoring data to date

The advanced condition monitoring tools and sensors have changed the decision making on maintenance in modern manufacturing. To face the change, an integrated 'prognostics-replacement' framework is proposed to optimise the replacement decision from component-level layer into production system-level layer by using condition monitoring data in this paper. Some special situations such as no failure or suspension histories of many of same or similar components for prognosis, etc., are considered. A novel degradation prediction model is introduced and the failure risk of a component is estimated based on its degradation level and service time. A total current-term cost rate function is defined to determine the replacement clusters and time for performing replacement from an integrated and economic view. A conservative window is used to adjust the replacement time and overcome the prognostic results varying at different inspection time in a long task. To optimise the replacement clusters effectively, a random-keys genetic algorithm (GA) based on convex set theory is developed. The proposed framework is validated by different small systems. Two commonly adopted replacement policies are compared. Sensitive analysis is conducted and the results show the outperformance of our proposed framework.