Dynamic maintenance strategy with iteratively updated group information

Maintenance grouping methods such as the rolling horizon approach are effective in reducing maintenance costs of multi-component systems. Despite the theoretical advancements of this approach, it still faces three challenges. First, the extensively adopted minimal repair assumption upon failures limits its application. Second, opportunistic maintenance upon corrective maintenance is overlooked, unable to fully take advantage of economic dependence. Third, maintenance plans are not based on actual maintenance history and health information, which may increase failure risks. To address these challenges, this paper formulates a novel dynamic planning framework that captures economic dependence in both preventive and opportunistic replacement. Unlike conventional approaches that restrict all maintenance activities into a finite planning horizon, our proposal focuses on activity-to-activity scheduling without specifying the horizon. As such, the subsequent maintenance schedule is dynamically updated once a system maintenance is executed. A flexible dynamic programming algorithm is developed to optimize the maintenance grouping, and the strategy framework is further extended to condition-based maintenance scenarios. The effectiveness and generality of the proposed maintenance strategy are demonstrated by numerical experiments.