A new multi-modal time series transformation method and multi-scale convolutional attention network for railway wagon bearing fault diagnosis

Abstract

To align with the evolving trends in intelligent railway wagon operation and maintenance and to enhance the precision of railway wagon bearing fault diagnosis, this paper introduces a novel method for bearing fault diagnosis. The method comprises two key innovations. Firstly, a multi-modal time series transformation method is proposed. This method extracts time series data from the original time domain signals via self-adaptive ensemble empirical mode decomposition with adaptive noise, transforms them into 2D matrices, and captures inter- and intra-period information relationships through convolution. Secondly, a multi-scale convolutional attention network is introduced, enriching fault information by utilizing parallel multi-scale convolution for down-sampling. To prevent feature loss, sliding convolution is adopted instead of pooling. Additionally, the model incorporates the convolutional block attention module to focus on critical information. Experimental validation conducted in a laboratory using a self-developed railway wagon bearing dynamic performance tester demonstrates high diagnostic accuracy and strong overall performance. The method’s generalizability is further confirmed through validation using publicly available datasets. This method could find practical use in railway maintenance, improving the accuracy of bearing fault diagnosis, and making operations more efficient.