A fault diagnosis method for railway signal equipment based on sample balancing and multi-granularity feature fusion

By addressing the challenges of sample imbalance and insufficient single-granularity feature extraction in railway signal equipment fault diagnosis, this paper proposes a fault diagnosis method based on sample balancing and multi-granularity feature fusion. First, the fault text data undergoes data cleaning followed by key information extraction to obtain a structured fault representation. Based on this representation, the RoBERTa-wwm model is employed to extract global deep semantic features, generating feature vectors. Second, the Borderline SMOTified GAN (BSMOTEG) method is introduced to augment minority class feature vectors, mitigating class imbalance. Subsequently, the balanced text vectors are fed into a BiLSTM network to capture temporal dependency features, while a multi-head attention mechanism is employed to weight local features. This achieves effective fusion of global-temporal-local multi-granularity features. Finally, a softmax classifier is employed for signal equipment fault diagnosis. Experimental analysis is conducted by using signal equipment failure text data recorded by a railway bureau’s electrical engineering department, with comparisons against other methods. Results demonstrate that the proposed method achieves optimal classification metrics.