An effective CNN-MHSA method for the fault diagnosis of ZPW-2000A track circuit

The mainstream methods for fault diagnosis of ZPW-2000A track circuits are overly complex in extracting features from sequence data, and their feature extraction capability is relatively limited, thus impacting the performance of fault diagnosis. To address this issue, we propose a fault diagnosis model for track circuits, named as convolutional neural network incorporating multi-head self-attention mechanism (CNN-MHSA). On one hand, the local features of sequence data are locally sensed and extracted by the convolutional layer; on the other hand, the global features of sequence data are captured by establishing long-distance dependency relationships through the multi-head self-attention layer. The dual extraction of local and global features enables accurate diagnosis of faults. Finally, we collect 27 fault modes and 2 normal operation modes in the simulation system and conduct a large number of numerical experiments. The experimental results show that the fault diagnosis accuracy of the CNN-MHSA model in this paper can reach 97.45%, which is an improvement of 0.64%, 0.44%, 0.44%, 0.44%, 0.33%, 0.22%, and 0.11% compared with models such as Decision Tree, Random Forest, CatBoost, long short-term memory (LSTM), convolutional neural network (CNN), and CNN-LSTM. It also has obvious advantages in evaluation metrics such as precision, recall, Macro-F1, and Micro-F1, as well as other evaluation metrics. Therefore, the model significantly improves the comprehensive performance of ZPW-2000A track circuit fault diagnosis and can be used as a more effective fault diagnosis method.