Dynamic optimization of condition-based maintenance strategies using Monte Carlo simulations to compare adaptive inspection intervals, threshold adjustments, and their impact on system performance

Condition-based maintenance (CBM) has emerged as a pivotal strategy for enhancing operational efficiency and minimizing maintenance costs in complex systems. This paper presents a comparative analysis of three adaptive maintenance strategies: the fixed-interval policy (ΔT,M) , the adaptive-interval policy (ΔT k ,M) , and the adaptive-threshold policy (ΔT,M k ) . The performance of each strategy is evaluated in terms of its impact on maintenance costs, system downtime, and reliability, using a stochastic degradation model based on the Wiener process. A Monte Carlo simulation framework is employed to simulate the degradation process and maintenance actions across a predefined operational horizon. The results demonstrate that the adaptive strategies consistently outperform the fixed-interval policy, reducing both maintenance costs and downtime while ensuring higher system reliability. Furthermore, penalties for reliability shortfall and excessive between-inspection risk are introduced to provide a comprehensive evaluation of strategy performance. This study contributes to the field of adaptive maintenance by highlighting the advantages of dynamic maintenance scheduling and threshold adjustment in managing operational risks and optimizing system performance.