A clustered blueprint separable convolutional neural network with high precision for high-speed train bogie fault diagnosis

Abstract

In this paper, a clustering method for KMedoids based on dynamic time warping (DTW-KMedoids) is designed to analyze multi-channel signals, and a lightweight network, clustered blueprint separable convolutional neural network (CBS-CNN), is established to perform fault diagnosis of high-speed train (HST) bogie. The motivation for proposing the novel method is to address the problems of large network size and high training cost in deep learning. First, DTW-KMedoids is adopted to cluster the channels of multi-channel signals. Second, based on the principles of blueprint separable convolution (BSConv) and mixed depthwise convolution (MixConv), a lightweight convolution model construction strategy called clustered blueprint separable convolution (CBS-Conv) is proposed, which uses the same blueprint to convolute the data of the channels in a cluster. Third, CBS-CNN is established, with multiple branches to process data from different clusters, and the computational result of each branch is connected by the proposed Connect layer. Finally, by virtue of the learned features from training, the model completes the end-to-end HST bogie fault diagnosis task, where the usefulness of CBS-CNN in detecting bogie failures including component performance degradation, component failures, and composite failures is validated. Further experiments show that CBS-CNN has a remarkable ability to adapt itself to different task environments and objectives.