Train Brake System Pipe Leakage Detection and Early Warning Method Based on Bayesian Networks

Abstract

This paper proposes a method for detecting and warning about leaks in train braking system pipelines based on Bayesian networks. Firstly, a detection model for pipeline leaks is established through the learning and inference of Bayesian networks. In the anomaly detection phase, the Bayesian network model is trained using historical data to monitor brake pressure abnormalities in real-time. Secondly, in the parameter regression calibration phase, the location and severity of the pipeline leaks are estimated based on the current brake pressure and relevant parameters. Finally, in the fault inference phase, the Bayesian network model is used to infer the possible causes of the leaks. The effectiveness and reliability of this method are verified through simulation design and actual data analysis. Compared to existing methods, this method can provide accurate leak detection and warning, thereby contributing to the safety of train operation. This research provides an effective method for detecting and warning about leaks in brake system pipelines and has practical application value.