ABSTRACT
Many papers are dedicated to maintenance strategies of deteriorating systems and almost all of them share a common assumption that the parameters of the degradation process are known. In this paper, we deal with a dynamic and aperiodic condition-based maintenance of single-unit systems with unknown parameters of deterioration. It has been considered that the deterioration is governed by an Inverse Gaussian (IG) process. The time interval between two successive inspections is scheduled based on the Remaining Useful Life (RUL) of the system. The Bayes method is employed to use the available information of degradation paths and update the information about parameters during the time. The proposed maintenance decision rule aims to avoid too frequent and costly inspections by implementing an aperiodic planning. The decision process is dynamically improved with the successive Bayesian update of the degradation parameters. The ability of the proposed modeling framework to drive the Bayesian update while controlling the number of inspection is analyzed through numerical experiments. The global maintenance cost is considered over a finite time horizon.
