Data-Driven Synthesis of Stochastic Fault Trees for Proactive Maintenance of Railway Vehicles

The spreading of sensor technologies has enabled railway operators to collect increasing amounts of granular data on relevant events of components and systems of railway vehicles and infrastructure, presenting unprecedented opportunities to develop predictive failure models. Our research introduces a novel methodology for synthesizing stochastic fault tree models by strategically integrating extensive diagnostic data logs, maintenance records, and domain-specific knowledge to predict component and system-level reliability dynamics. To demonstrate the potential of the approach, we apply it to the traction control unit of a fleet of regional passenger trains, showing a scalable framework for predictive failure assessment across diverse railway vehicle configurations. By leveraging existing diagnostic infrastructure without requiring additional sensor investments, our approach represents a pathway from reactive diagnostic practices to proactive maintenance strategies.