Reliability Analysis of Urban Rail Vehicle Traction Systems Based on Monte Carlo Simulation and Dynamic Fault Trees

Abstract

The reliability of traction systems in urban rail transit vehicles is critical to safe and efficient operations. However, existing reliability assessment methods face challenges such as state space explosion, computational complexity, and difficulties in modeling fault interdependencies and imperfect maintenance. This paper proposes an integrated framework that combines dynamic fault trees with fault dependency models and Monte Carlo simulation. The method captures fault dependencies through functional dependency gates incorporating fault impact factors, models component degradation using a two-parameter Weibull distribution, and accounts for imperfect maintenance through a age reduction factor. The simulation efficiency of three random number generators—LCG, MT, and PCG64—was compared. A Monte Carlo simulation of 100,000 trials under maintenance-free conditions yielded a system MTBF of 29.9 months. A mode significance analysis identified the pantograph control unit and the traction control unit as the most critical components. Under targeted maintenance based on component criticality, the MTBF increased to 39.77 months—a 33% improvement—while the peak failure rate was maintained at approximately 3%. The MT generator exhibited the fastest convergence, achieving stability within 1% after 3000 iterations. This framework provides a practical foundation for optimizing preventive maintenance strategies for urban rail transit systems, and sensitivity analysis confirmed the robustness of weak link identification to parameter variations. This method is applicable to other complex systems with interdependent failures and multiple maintenance schedules.