Simulation of Automatic Identification Model of High-Speed Rail Cracks Based on Deep Learning

Aiming at the rail cracks in high-speed railway, this paper proposes and studies an automatic crack identification model based on deep learning, and evaluates its performance through simulation experiments. In modern railway transportation, the safety of high-speed railway is very important, and the timely detection of cracks plays a key role in ensuring the normal operation of the transportation system. In the simulation process, we use Singular Value Decomposition (SVD) and other technologies to further analyze the crack signal, which enhances the interpretability of the model output results. This not only helps to understand the basis of crack discrimination by the model, but also provides more reference for the practical application of the model. In this paper, convolutional neural network (CNN) and other deep learning frameworks are used to automatically identify cracks in high-speed rail crack signals. Through large-scale data set training, the model has learned the diversity of cracks and has strong generalization ability. The simulation results show that the deep learning model has achieved remarkable accuracy and robustness in crack detection tasks, and has obvious advantages compared with traditional methods.