A High-Speed Train Traction Motor State Prediction Method Based on MIC and Improved SVR

The traction motor realizes the mutual conversion of electrical energy and mechanical energy during the train traction and braking process and is a key component of high-speed trains. The normal operation of the motor is directly related to the safety of high-speed train operation. Changes in temperature signals can reflect faults in the traction motor. By analyzing the internal and external influencing factors of temperature signals, a multi-factor prediction model for traction motors is established based on the maximal information coefficient and improved support vector regression. In this model, highly relevant features selections are performed based on time-delayed sequences and the maximal information coefficient. Using the adaptive particle swarm algorithm to optimize the improved support vector regression algorithm can enhance its accuracy and efficiency. Furthermore, using the K-nearest neighbor algorithm for error prediction will yield more accurate results. By comparing the RMSE, MBE, MAE, and other evaluation metrics of different algorithms under various working conditions, the results show that the prediction method proposed in this paper performs well across different working conditions. This method demonstrates greater adaptability to varying conditions and is more suitable for applications involving high-speed trains.