Impact-Based Maintenance Efficiency Modeling for Multi-Component Systems With Condition-Based Component Selection

This paper develops an impact-based maintenance efficiency model that dynamically links maintenance effectiveness to the number and criticality of replacement components in multi-component repairable systems. Unlike existing approaches that assume static efficiency parameters, the model enables maintenance decisions that adapt to actual system degradation states. The efficiency formulation decomposes into a risk fraction capturing the proportion of weighted system degradation addressed, and a coverage factor with a diminishing-returns parameter reflecting the criticality distribution of maintained components. A condition-based selection rule achieves natural component rotation without explicit constraints, ensuring an equitable distribution of maintenance efforts while prioritizing high-criticality degraded components. Statistical validation using maximum likelihood estimation demonstrates parameter recovery within 2–8% of true values, while predictive performance evaluation shows 93% reduction in failure prediction error compared to approaches ignoring maintenance effects. A case study on a heterogeneous 20-component industrial system demonstrates that the proposed condition-based approach achieves a 30% cost reduction versus a no-maintenance baseline and a 5% improvement over fixed-selection strategies. Furthermore, the natural rotation mechanism maintains 65% of system components across maintenance epochs.