Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Abstract

Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

A Multi-Factor Driven Model for Locomotive Axle Temperature Prediction Based on Multi-Stage Feature Engineering and Deep Learning Framework

“…Therefore, scholars have proposed various artificial intelligence algorithms to establish accurate and effective forecasting models for further improvement. Liu applied the backpropagation neural network (BPNN) to predict trains' axle temperatures and exceed GM (1, 1) with better accuracy [18]. Abdusamad proposed multiple linear regression (MLR) in future temperature forecasting [19].…”

A New Hybrid Ensemble Deep Learning Model for Train Axle Temperature Short Term Forecasting

“…The deep learning algorithms contain a complex learning structure, in which the hidden layers lead to better learning accuracy by massive data than the statistical methods and traditional machine learning methods. Hao and Liu [26] proposed the Back Propagation Neural Network (BPNN) in the axle temperature prediction for high-speed trains. The comparative results have proved that the forecasting error of BPNN is lower than the GM (1,1) model.…”