Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes

Abstract

Air leakage in braking pipes is a commonly encountered mechanical defect on trains. A severe air leakage will lead to braking issues and therefore decrease the reliability and cause train delays or stranding. However, air leakage is difficult to be detected via visual inspection and therefore most air leakage defects are run to fail. In this research we present a framework that not only can detect air leakages but also predicts the severity of air leakages so that action plans can be determined based on the severity level. The proposed contextual anomaly detection method detects air leakages based on the on/off logs of a compressor. Air leakage causes failure in the context when the compressor idle time is short than the compressor run time, that is, the speed of air consumption is faster than air generation. In our method the logistic regression classifier is adopted to model two different classes of compressor behavior for each train separately. The logistic regression classifier defines the boundary separating the two classes under normal situations and models the distribution of the compressor idle time and run time separately using logistic functions. The air leakage anomaly is further detected in the context that when a compressor idle time is erroneously classified as a compressor run time. To distinguish anomalies from outliers and detect anomalies based on the severity degree, a density-based clustering method with a dynamic density threshold is developed for anomaly detection. The results have demonstrated that most air leakages can be detected one to four weeks before the braking failure and therefore can be prevented in time. Most importantly, the contextual anomaly detection method can pre-filter anomaly candidates and therefore avoid generating false alarms. To facilitate the decisionmaking process, the logistic function built on the compressor run time is further used together with the duration of an air leakage to model the severity of the air leakage. By building the prediction model on the severity, the remaining useful life of the air braking pipe until it reaches a certain level of severity can be estimated.

Keywords

• anomaly detection
• severity prediction
• air leakage
• braking pipe
• rolling stock