High-Accuracy and Adaptive Fault Diagnosis of High-Speed Train Bogie Using Dense-Squeeze Network

Abstract

As one of the most important systems of high-speed train (HST), bogie system matters when it comes to the safety and reliability of HST operation. In order to strengthen feature propagation and alleviate vanishing gradients problems in training deep-learning networks, a novel Dense-Squeeze Network based on one-dimensional convolutional neural networks (1D-CNN) is proposed for bogie fault diagnosis. On the one hand, dense blocks are capable of facilitating feature reuse and increasing the depth of feature propagation. On the other hand, the squeeze operation automatically measures the importance level of each feature channel, and then enhance the useful features and suppress the useless features for the current task. The experimental results tested by the HST model CRH380 A at different speeds verify the effectiveness of the proposed method, and the accuracy of fault diagnosis converges to 99.66%. Compared with other deep-learning-based methods, such as 1D-CNN, Long Short-Term Memory (LSTM) and DenseNet, the superiority of the proposed scheme is demonstrated clearly.