Multimodal imbalanced-data fault diagnosis method based on a dual-branch interactive fusion network

Bearing-fault diagnosis in rotating machinery is essential for ensuring the safety and reliability of mechanical systems. However, under complicated working conditions, the number of normal mechanical equipment samples can far exceed the number of faulty ones. When the data are so imbalanced, data fault diagnosis cannot be easily conducted using conventional deep learning methods. This study proposes a fault diagnosis method based on a dual-branch interactive fusion network, which improves the accuracy and stability of bearing-fault diagnosis. First, a dual-branch feature representation network comprising an iterative attention-feature fusion residual neural network and a long short-term memory network is designed for extracting different modal features. Meanwhile, intermodal fusion of the extracted features is performed through multilayer perception. Based on the cost-sensitive regularization loss, a new joint loss function is then designed for network training. Finally, the effectiveness of the proposed method is verified through comparative experiments, visualization analyses, ablation experiments, and generalization performance experiments.