Since the fault of the yaw damper during service will seriously threaten the safety of train operation,a fault diagnosis method for the yaw damper was proposed based on convolutional neural network. The time-frequency spectrum of the collected damping force signal was obtained by short-time Fourier transform, and it was divided into a training set and a test set. Then, the training set was input into the convolutional neural network model, and the characteristics of the training sample signal were carried out. Further, the specific model of the convolutional neural network was obtained through forward propagation and back propagation, and the network parameters were updated through multiple iterations. Finally, the trained model was used for the test set to obtain the fault diagnosis accuracy of the yaw damper. In order to verify the validity of the model, the data of normal state, poor startup and sawtooth wave fault were selected as experimental verification. The results show that the proposed method not only avoids the problems caused by the need for manual feature extraction, but also has a better diagnosis effect.
CHEN Guang, MA Wenda, SUN Zeming, ZHANG Wan. Fault Diagnosis of Yaw Damper in High-Speed Train Based on Convolutional Neural Network[J]. Machine Tool & Hydraulics,2023,51(8):194-199