A Monte Carlo Method to Decision-Making in Maintenance strategies

Abstract

Health prognosis is an advanced approach for anticipating the future status of systems, structures, and components. While it is accepted as an important step in boosting maintenance performance and resilience of a system, the subject of post-prognosis maintenance decision-making remains unsettled. To address this problem, we present We present one of the most effective economic criteria for concurrently assessing the performance and resilience of the time-based (TBM) and condition-based maintenance methods (CBM). This criteria is a linear combination of the asymptotic average cost per unit of time and the standard deviation of the Mean Cost Per Renewal Cycle (MCPRC) of maintenance charges per renewal cycle. Ultimately, we will evaluate these two maintenance procedures to select the one that gives the optimum mix of lifetime and robustness for our system. We will also study how to fine-tune our new criteria to obtain the ideal balance of performance and robustness for two systems, the first is a system with changeable behavior, while the second one presents a system with more or less stable behavior. The inclusion of the Monte Carlo Method improves the comparative study of maintenance methods, delivering insights into the performance and resilience of each adaptation in decision-making.