Research on Electromagnetic Interference Identification of Track Circuit Equipment Based on CNN Transformer

This paper proposes a track circuit interference identification model, which combines convolutional neural network (CNN) and transformer architecture to identify common types of electromagnetic interference in track circuit equipment. The model maps the time-frequency characteristics of the input monitoring signal into high-dimensional features through the deep learning model, and classifies the interference modes. Subsequently, a variety of common interference signals are generated for experimental verification, and the proposed model performs well on the test data. Ablation experiments show that the combination of convolutional neural network and attention mechanism can effectively improve the classification performance of interference.