Probabilistic Risk Analysis for Catenary System of Heavy-Haul Railway Based on Casual Inference

Abstract

The reliability of the catenary system is crucial for the safety and efficiency of heavy-haul railways. This study presents a probabilistic risk analysis model for the catenary system, employing causal inference methods to capture the complex relationships among risk factors. Using historical operational data, we identify key risk contributors such as environmental conditions, vehicular loads, and equipment failures. By combining fault tree analysis (FTA) and failure mode and effects analysis (FMEA), we establish risk propagation pathways. The proposed method utilizes Bayesian networks to quantify conditional probabilities and trace the causal chains leading to potential failures. Through reverse inference, we identify critical risk nodes and their impact on system performance. This approach enhances the accuracy of risk assessment and provides an effective tool for proactive risk management in heavy-haul railways, aiding in the optimization of maintenance strategies and strengthening the resilience of the catenary system under varying operational conditions.