Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism

This paper proposes a novel method called Fusion Attention Network for Bearing Diagnosis (FAN-BD) to address the challenges in effectively extracting and fusing key information from current and vibration signals in traditional methods. The research is validated using the public dataset Vibration, Acoustic, Temperature, and Motor Current Dataset of Rotating Machines under Varying Operating Conditions for Fault Diagnosis. The method first converts current and vibration signals into two-dimensional grayscale images, extracts local features through multi-layer convolutional neural networks, and captures global information using the self-attention mechanism in the Vision Transformer (ViT). Furthermore, it innovatively introduces the Channel-Based Multi-Head Attention (CBMA) mechanism for the efficient fusion of features from different modalities, maximizing the complementarity between signals. The experimental results show that compared to mainstream algorithms such as Vision Transformer, Swin Transformer, and ConvNeXt, the Fusion Attention Network for Bearing Diagnosis (FAN-BD) achieves higher accuracy and robustness in fault diagnosis tasks, providing an efficient and reliable solution for bearing fault diagnosis.The proposed model outperforms ViT, Swin Transformer, ConvNeXt, and CBMA-ViT in terms of classification accuracy, achieving an accuracy of 97.5%. The comparative results clearly demonstrate that the proposed Fusion Attention Network for Bearing Diagnosis yields significant improvements in classification outcomes.