Group maintenance scheduling for two-component systems with failure interaction

• Published in: Applied Mathematical Modelling Vol. 71; pp. 118 - 137
• Main Authors: Yang, Li, Zhao, Yu, Ma, Xiaobing
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.07.2019; Elsevier BV
• Subjects: Cost optimization; Dependence; Economic impact; Failure; Failure interaction; Industrial engineering; Industrial plants; Maintenance; Maintenance management; Manufacturing engineering; Multi-component system; Scheduled window; Search algorithms; Swarm intelligence; Multi-component system; Failure interaction; Maintenance; Scheduled window; Cost optimization
• ISSN: 0307-904X; 1088-8691; 0307-904X
• DOI: 10.1016/j.apm.2019.01.036

•A novel failure interaction framework is constructed considering hazard rate increment.•Environmental uncertainties are incorporated into dependent failure processes.•A new group maintenance strategy is developed to prevent dependent failures.•The applicability is validated by a case study on an electrical distribution system. Failure dependence is a common phenomenon in complex and large-scale industrial plants, which may cause tremendous economic losses and unacceptable safety risks. This paper investigates an advanced group and opportunistic maintenance policy for a two-component system with failure interaction. The interaction between soft and hard failures is characterized by random hazard rate increments, with fully consideration of environmental uncertainties. To sufficiently utilize the economic dependence between components, Scheduled maintenance windows are equally spaced for group maintenance of the whole system. Whenever a component is replaced at a maintenance window, the other one is also opportunistically replaced. Furthermore, failure of component 2 is removed by minimal repair, whereas replacement is left to the subsequent window. The objective is to jointly optimize the maintenance interval and the number of maintenance windows via the artificial bee colony algorithm. A case study on an electrical distribution system is provided to validate the applicability of the adopted policy.