Sound signal-based lightweight fault diagnosis for railway turnout system via combining transformer and CNN

Abstract Significant progress has been made in deep learning-based fault diagnosis for railway turnout system. However, the increasing complexity of networks has resulted in higher network parameters and computational demands, making it necessary for algorithms to meet more stringent hardware requirements. Therefore, there is a need for a lightweight deep learning approach that can effectively diagnose faults in railway turnout system while maintaining high performance. In this research, we propose a lightweight model integrating Transformer and Convolutional Neural Networks (CNN) for fault diagnosis in railway turnout systems. The model combines CNN and Transformer to extract both local and global features from monitoring signals and introduces lightweight enhancements. Specifically, a Multi-Scale Depthwise Dilated Convolution (MDDC) block is introduced into the CNN to break down cross-channel convolutions into pointwise and depthwise convolutions, with multiscale dilated convolution kernels applied in the depthwise convolutions. Additionally, an Context-Aware Attention Module (CAAM) is integrated into the Transformer to simplify complex matrix multiplications and multidimensional exponential operations. Experimental results using sound signals collected during actuator operations demonstrate that the optimized algorithm reduces the number of parameters and computational complexity by 75% compared to the baseline model, while also reducing inference time by one-third. The optimized algorithm achieves an accuracy of 99.45%. These findings lay the groundwork for further optimization of fault diagnosis in railway turnout system.