CNN-LSTM Networks Based Fault Diagnosis Using Spatial and Temporal Information for ZPW-2000A Track Circuit

Fault diagnosis plays an important role in production and life. Rapid and reliable detection and identification of track circuit faults are key to ensuring train operation efficiency and safety. This article proposes a ZPW-2000A track circuit fault diagnosis method based on convolutional neural network and long short-term memory network. By considering the signals from multiple track circuits within a geographic area, the faults can be diagnosed based on their spatial and temporal dependencies. To train and test the network, an equivalent circuit model based on the basic structure and working principle of the ZPW-2000A track circuit is established to obtain experimental data. The experimental results show that the model can meet the requirements of actual track circuit operation, and the network can accurately classify the input test samples. Compared with other fault diagnosis methods, this method has been proven to be superior.