A Dynamically Weighted Opportunistic Maintenance Method for Critical Equipment Based on Dominant Downtime‐Loss Intervals

Opportunistic maintenance of critical equipment must account for degradation, maintenance opportunities, and the calendar‐dependent consequences of downtime. Existing approaches often represent time‐varying losses as objective coefficients, risk priorities, or rescheduling conditions, but do not explicitly transmit future high‐consequence periods into current maintenance actions. This paper proposes a Dynamic Loss‐Driven Risk‐Integrated Coordinated Opportunistic Maintenance method based on dominant downtime‐loss intervals (DL‐DRICO). The method identifies such intervals from the lower bound of a rolling proportion of associated downtime loss in total cost, uses the resulting signal to dynamically weight the relevant loss component, and combines it with a reliability–economic dual criterion to determine whether PM or PR should be advanced at an available opportunity window. A data‐supported imperfect‐maintenance degradation model is used, with the number of Weibull stages selected by nested one‐, two‐, and three‐stage model screening rather than prescribed a priori. In an assembly‐line inverter case study, DL‐DRICO is compared with TBM, ABR, MABR, and an information‐matched multi‐stage opportunity‐window age‐based replacement baseline (MS‐OW‐ABR). Across 200 common‐random‐number replications, DL‐DRICO reduces the annual average cost rate by 30.53%, 20.10%, 9.91%, and 7.28%, respectively, while also reducing unplanned failures and associated downtime. Ablation results indicate that dominant‐interval identification and opportunity‐window coordination are the principal mechanisms underlying the improvement.