Semantic-aware fault diagnosis of heavy-duty railway maintenance machinery and its potential in multisensor fusion...

To address the semantic gap in physical sensor data for fault diagnosis of heavy-duty railway maintenance machinery and the underuse of semantic information in maintenance logs, this study proposes a model that treats fault-related text as a virtual semantic sensor. The goal is to explore a semantic-aware approach to fault diagnosis and its role in multisensor fusion. A classification model combining a BERT pretrained model with a convolutional neural network (BERT-CNN) was built. To improve the focus on key semantic units and strengthen links between textual features and sensor modalities, a dual self-attention (DSA) mechanism was added, forming the BERT-DSA-CNN model. It extracts structured semantic feature vectors from unstructured logs, which serve as outputs of the virtual semantic sensor. Experiments show that (1) incorporating DSA significantly increases performance, with BERT-DSA-CNN and Word2vec-DSA-CNN outperforming baselines (BERT-CNN and Word2vec-CNN) in terms of accuracy, precision, recall, and F1-score; (2) BERT's contextual embeddings clearly surpass Word2vec, as BERT-DSA-CNN consistently outperforms Word2vec-DSA-CNN; (3) CNN effectively captures local features of short fault texts, as BERT-CNN outperforms BERT-BiLSTM on most metrics; and (4) deep semantic feature learning substantially outperforms traditional machine learning, confirming the superiority of deep semantic feature learning. This study validates that the proposed semantic-aware model can efficiently transform fault texts into semantic features for identification. More importantly, the structured semantic features extracted by this model have the potential to be fused with physical sensor data in future work, which could provide a foundation for more accurate, robust, and interpretable intelligent fault diagnosis systems for heavy-duty railway maintenance machinery.