Research on Railway System Reliability and Its Development Trends

With the continuous advancement in the intelligence of railway systems and the increasing complexity of their service environments, traditional reliability assessment methods face significant challenges in dynamic risk modeling, the systematic integration of intelligent technologies (big-data predictive maintenance, AI fault diagnosis), and cross-subsystem collaborative analysis. This study aims to systematically organize reliability assessment indicator systems and proposes multi-dimensional construction principles encompassing probability-based (reliability), time-based (MTBF, MTTR), and ratio-based (failure rate) metrics. Through an in-depth comparison of the technical characteristics and applicability of Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), and the Markov method, combined with research practices on key subsystems such as the traction power supply system (failure probability modeling considering harmonics and load characteristics), track infrastructure (operational safety reliability assessment based on track irregularity data), and communication and signaling systems (reliability comparison of TACS and CBTC architectures), the significant limitations of existing methods are revealed. These limitations include difficulties in handling dynamic fault logic, analyzing multi-state coupling characteristics, and capturing cascading failure effects.Future development trends necessitate the integration of Dynamic Bayesian Networks (DBNs) (to enable automatic fault propagation inference) and digital twin simulation technology (to construct multi-scenario validation platforms). This integration will establish a data-driven (integrating real-time monitoring, historical fault databases, and operation and maintenance logs) full lifecycle assessment framework. Furthermore, exploring the application of Model-Based Systems Engineering (MBSE) and blockchain traceability technology for embedding reliability requirements and enabling closed-loop management is crucial. By constructing a reliability analysis system characterized by dynamic adaptability, environmental robustness, and engineering practicality, this research provides solid theoretical support and engineering assurance for intelligent railway systems to transition their risk prevention and control mode from passive maintenance to proactive prevention.