A Novel Irregularity Detection Method for Railway Tracks Based on Improved Masked Sparse Transformer

With the continuous increase of train speed, track irregularity poses a serious threat to the safety and comfort of train operation. In this article, a track irregularity detection method based on an improved masked sparse Transformer (MST) is proposed to alleviate the contradiction between detection efficiency and accuracy. By analyzing the coupling mapping relationship between the dynamic offset of the train body and track irregularity, a lightweight, sparse Transformer model is constructed. The sparse attention mechanism based on double exponential attenuation is adopted in this model, and the algorithm complexity is significantly reduced, while the decoder structure is simplified to further reduce the number of model parameters and improve the detection efficiency. The experimental results show that the proposed method is superior to the traditional recurrent neural network (RNN) and the standard Transformer; the track irregularity can be detected in real time with high precision, and the reliability of the model is verified by an interpretable analysis. The research in this article provides an efficient and low-cost solution for track condition monitoring, which has important engineering application value.