A small-amplitude hunting motion recognition method based on transfer learning

Abstract

The hunting motion of the vehicle increases the wear of the wheel tracks and affects the lateral stability of the vehicle system and safety, so the hunting motion of the vehicle needs to be recognized. However, the existing recognition methods ignore the small-amplitude hunting motion that precedes the onset of the running motion. In addition, due to the scarcity of real hunting data of high-speed trains, there is a problem of underfitting when training with traditional deep learning methods. In this paper, first, the dynamics model of a high-speed train is established, and the normal, small-amplitude hunting and hunting motions of high-speed trains in the process are simulated. Second, this paper proposes a transfer learning-based method for high-speed train hunting motion recognition. The method uses easily collected normal data samples in the training process, does not use real data samples of small-amplitude hunting and hunting motions, and completes the high-speed train running motions recognition task by transferring from simulation data (source domain) to real data (target domain). Finally, the validation is carried out using the real data, which proves that the method-related approach has some engineering application value in the intelligent monitoring of high-speed trains.