The Catenary Fault Prediction and Health Management System is driven by full-lifecycle data generated during catenary construction and operational use—including design parameters, inspection and monitoring data, offline testing, defects, faults, and maintenance records. It employs cloud computing, big data technology, and artificial intelligence algorithms such as deep learning and fuzzy reasoning to establish a catenary RAMS (Reliability, Availability, Maintainability, and Safety) evaluation index system. The system assesses the health status of the catenary, achieves fault prediction and lifespan prediction for the catenary, and provides optimal maintenance strategies. This reduces maintenance costs and accident risks, enabling the transition of catenary maintenance from "condition-based maintenance" to "predictive maintenance."