Fault diagnosis method for bearing of high-speed train based on multitask deep learning

Abstract

High-speed trains often pass through tunnel, turnout, ramp, bridge, and other line features in the process of running. At the same time, the length of the operation time, weather conditions, changes in train running conditions, and other conditions will lead to the loss of the train. In view of the complexity of a high-speed train structure and operation environment, in order to effectively evaluate the health of the train in the operation process, this paper proposes a diagnosis method of bearing temperature anomaly of a high-speed train based on condition identification and multitask deep learning. In this paper, the important components of bogie axle box, gearbox, and traction motor are taken as the research object. Firstly, the operating condition parameters of the high-speed train are analyzed and identified, and the K-means algorithm is used to classify and identify the operating condition of the high-speed train. Then, based on the operating condition identification and multitask deep learning, the bearing temperature prediction model is constructed. In addition, according to statistical quality control theory, the difference between the value predicted by the model and the real value is used to diagnose the anomaly of the bearing temperature of the high-speed train. Finally, the accuracy and availability of the model are verified by an example. The model can judge whether the running train bearing temperature is in the normal range in real time and predict and alarm the abnormal bearing temperature.