The paper established a two-parameter Weibull distribution model to analyze the failure law of key components of metro vehicles and employed the least squares method to fit the parameters of the Weibull function. The paper provided a detailed introduction of the model's parameter fitting method based on actual historical failure data. Taking the EDCU (Electrical Door Control Unit) as an example, the failure rate, cumulative failure rate, and reliability of EDCU for 3 different vehicle models were calculated, and the calculation results were verified to conform to the Weibull distribution through Q-Q diagrams. The results show that although the designed service life of EDCU of vehicle door system is 15 years, the reliability of the EDCU decreases significantly around 10 years. The EDCU reliability of the three vehicle models declines by 22%, 4.2%, 10.6% and the failure rates are 2.7%, 0.57%, 1.13%. Based on the reliability of EDCU mentioned above, while ensuring the reliability of components, the maintenance methods of components can be optimized to reduce maintenance costs.

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.