Damage Detection of Rail Fastening System Through Deep Learning and Vehicle-Track Coupled Dynamics

Abstract

Owing to the rapid development of the rail transportation, the health monitoring of the track structure becomes a challenging problem. This article presents a novel approach to carry out damage detection and localization of fastening systems along the rail based on deep learning and vehicle-track coupled dynamics analysis. A convolutional neural network (CNN) is designed to learn optimal damage-sensitive features from the rail acceleration response automatically and identify the damage location of fastening systems, leading to a high detecting accuracy. The vehicle-track coupled dynamics model incorporating different damage level of fastening systems is adopted to generate labeled dataset to train the proposed network. The advantage of this approach is that CNN learns to extract the optimal damage-sensitive features from the raw dynamical response data automatically without the need of computing and selecting hand-crafted features manually. T-SNE is applied to manifest the super feature extraction capability of CNN. Thereafter, the trained network is estimated on the testing dataset to validate its generation capability. The results reveal a good performance of the proposed method.