A Study of the Impact of Predictive Maintenance Parameters on the Improvment of System Monitoring

Abstract

Predictive maintenance can be efficiently improved by studying the sensitivity of the maintenance decisions with respect to changes in the proposed model parameters (costs, duration of reparation, etc.). To address this issue, we first propose an original approach that includes both maintenance costs and maintenance risks in the same objective function to minimize. This approach uses the RUL as an indicator of the health state of the system and supposes that the system is under regular inspections and can only be replaced by a new system in case of serious deterioration or failure. Then, we present a process of human decision making under uncertainty based on several criteria. Finally, we study and analyze the influence of the model parameters and their implications on the obtained maintenance policies. The study will lead to some recommendations that can improve the predictive maintenance decisions and help experts better handle maintenance costs.