Rolling horizon optimal maintenance policy for a system subject to shocks and degradation under uncertain parameters

• Published in: Computers & industrial engineering Vol. 157; p. 107298
• Main Authors: Haghighi, Firoozeh, Castanier, Bruno, Misaii, Hasan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2021; Elsevier
• Subjects: Computer Science; Degradation-threshold-shock model; Engineering Sciences; Mathematics; Optimal maintenance policy; Parameter uncertainty; Rolling horizon approach; Statistics; Optimal maintenance policy; Rolling horizon approach; Degradation-threshold-shock model; Parameter uncertainty
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2021.107298

•Developing a new degradation-threshold-shock model.•Introducing the impact of shocks on the degradation via the virtual age concept.•Proposing an optimal maintenance policy under uncertainty. In this paper, we consider a single-unit system which is subject to shocks and deterioration over time. We assume that the shocks occur based on a counting process. Each incoming shock affects the system in two ways; the fatal shock causes the system failure whereas the effective shock weakens the system by increasing its age. The system degradation is modeled based on a general degradation path (GDP) model which depends on the impact of shocks received during system operation and the system virtual age. Both soft and hard failures are considered and a new degradation-threshold-shock (DTS) model is proposed. The main focus of this paper is to provide a dynamic maintenance policy based on the current conditions of the system while both environmental and operational conditions of the system are uncertain. In this regard, a condition-based maintenance (CBM) policy with periodic inspection is developed. Both corrective and preventive replacements are taken into account and preventive replacement threshold is updated at each inspection time. The update is done in the light of new information on the number and magnitude of shocks using a rolling horizon approach within the Bayesian framework. A simulation study has been conducted to show the applicability and efficiency of the proposed method and the effect of prior information on the maintenance decisions has been studied.