Multi-Modal Attention-Based Automatic Diagnosis of Bearing Vibration Faults for Robust Mechanical Condition Monitoring

Bearing components are critical elements of rotating machinery, and their failures can lead to sudden shutdowns, reduced efficiency, and increased maintenance costs. To address the limitations of conventional fault diagnosis methods, this study proposes a multimodal deep learning framework that integrates raw time-domain vibration signals with stationary wavelet transform (SWT)-based time-frequency representations using a novel bidirectional cross-attention mechanism. This architecture enables effective and mutual interaction between heterogeneous features, significantly enhancing discriminative representation learning and fault classification performance in complex operating environments. The proposed model was evaluated on the public SUBVF1.0 dataset under three fault conditions, achieving training and validation accuracies of 99.17% and 95.63%, respectively. The model also demonstrated strong performance in terms of precision, recall, F1-score, and ROC analysis. An ablation study further confirms the contribution of each model component. These results indicate that the proposed approach is accurate, robust, and suitable for practical industrial fault diagnosis applications.