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

Abstract

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.

Keywords:

• prognosis
• opportunistic replacement
• long task horizon
• current-term cost rate
• random-keys GA

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research is funded partially by the National Natural Science Foundation of China [grant number 51705321] and [grant number 51505288], the Fundamental Research Funds for the Central Universities [grant number 2232019D3-29] and the China Postdoctoral Science Foundation [grant number 2017M611576].