LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation

Abstract

Rail corrugation has a significant impact on the safety of high-speed railway operations, making its identification particularly important. Traditional manual inspection methods are infeasible for large-scale identification within limited time frames, while existing methods based on machine vision or axle box acceleration face challenges such as high costs, complex equipment installation and maintenance, as well as difficulties in achieving real-time performance. To address these challenges, this study proposes an innovative low-cost real-time recognition network (LCRTR-Net), which utilizes accelerometers installed on the underside of the train body and combines wavelet packet decomposition with dilated causal convolution in a residual neural network. Specifically, the approach first extracts the latent features of train body acceleration caused by rail corrugation through wavelet packet decomposition and reconstruction. Next, dilated causal convolution is employed to capture the temporal causal relationships and long-term dependencies of these latent features. Finally, the integration of residual connections further enhances the feature extraction performance and computational efficiency of LCRTR-Net. Experimental results demonstrate that LCRTR-Net exhibits significant generalization ability and real-time performance, achieving an average recognition accuracy exceeding 97.0%, with a recognition time of only 0.17 ms per rail corrugation sample, significantly outperforming existing rail corrugation recognition methods. This indicates that LCRTR-Net has broad application potential in practical railway operations. Future research directions will focus on unsupervised or few-shot learning algorithms and multi-sensor integration to further improve recognition accuracy and real-time performance, promoting the practical application of this technology.