RCM-based maintenance interval decision model for suburban EMUs

Traditional daily inspections of suburban Electric Multiple Units (EMUs) often result in either excessive maintenance or insufficient maintenance, leading to increased operational and maintenance costs as well as inefficient resource utilization. This study proposes a maintenance optimization method for the daily inspection interval of suburban EMUs based on the reliability-centered maintenance (RCM) framework. Initially, a Potential Failure Detection Rate (PFFR) model is developed to quantitatively analyze historical maintenance and failure data of the vehicles. Subsequently, the failure risk of critical components is dynamically assessed by integrating failure rate indicators. By combining RCM principles with probabilistic statistical models, the method accurately identifies components with elevated failure probabilities and establishes differentiated maintenance strategies for each unit, effectively balancing maintenance effort and risk control. Case studies indicate that the optimized daily inspection procedures reduce man-hours by 55%. The findings provide data-driven support for extending the daily inspection interval of suburban EMUs while achieving improvements in both safety and operational efficiency.