DLeNN-Attention-Based Fault Diagnosis for Railway Turnout Power Data Under Limited-Data Conditions

Railway turnout systems are important components of railway signaling infrastructure, and timely fault diagnosis is essential for ensuring operational safety and maintenance efficiency. In practical applications, turnout fault diagnosis based on power data is often challenged by limited fault samples and severe class imbalance. To address these issues, this paper proposes a DLeNN-Attention-based fault diagnosis method for railway turnout power data, where DLeNN-Attention denotes Dilated-LeNet5-Attention. First, the original power sequences are standardized to a unified length through truncation, zero-padding, and normalization. Then, a hybrid data augmentation strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) and a generative adversarial network (GAN) is adopted to enrich minority fault samples and alleviate class imbalance. Based on the augmented data, a DLeNN-Attention model is designed by integrating dilated convolution with the Convolutional Block Attention Module (CBAM), so as to capture richer temporal characteristics and enhance discriminative fault-related information. In this way, the proposed method can effectively learn representative features from turnout power data and improve fault classification performance. Experimental results on S700K turnout power data demonstrate that the proposed method achieves better diagnosis performance than several baseline models. The results indicate that the proposed method is effective for turnout fault diagnosis under limited-data conditions and shows promising application potential in intelligent health monitoring of railway turnout systems.