# Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism
###### Abstract  
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. b 2024 21 January 2025 29 Januarybased on wavelet packet transform and cascaded stochastic quantization. This method can generate high-quality pseudo-samples to address the unbalanced sample problem in bearing fault diagnosis, improving the diagnosis performance [8]. However, these methods often depend on complex signal processing techniques and experience, and manual feature extraction has drawbacks such as strong subjectivity and poor universality. Moreover, shallow models perform poorly in handling complex nonlinear problems, limiting the effectiveness and applicability of traditional diagnostic methods. Therefore, it is necessary to overcome the limitations of traditional methods and shallow models while effectively integrating and fusing vibration and current signals for fault diagnosis. In recent years, deep learning has played an important role in solving these problems, especially regarding the successful application of deep convolutional neural networks (CNN) in image processing, which has improved research progress in the field of fault diagnosis [9]. CNN can effectively extract key features from rolling bearing fault data, thereby improving the accuracy and efficiency of fault diagnosis. For example, [PERSON] et al. [10] combined Attention CNN (ACNN) and BiLSTM for rolling bearing fault diagnosis. They processed raw vibration signals using a wide one-dimensional CNN to extract their spatial features, which are enhanced by the Convolutional Block Attention Module (CBAM), which prioritizes important features. These features are then passed to BiLSTM for temporal feature extraction. The model is robust against noise interference and does not require preprocessing. While it achieves high accuracy even in noisy environments, its drawbacks include longer training times and potential instability under extreme noise conditions. Additionally, it does not address data imbalances. [PERSON] et al. [6] conducted detailed fault diagnosis experiments based on wavelet analysis and neural networks to extract feature vectors from rolling bearing signals. They compared fault diagnosis methods based on BP neural networks and radial basis function neural networks, and analyzed the diagnostic results based on the relaxation neural networks and closed neural networks. [PERSON] et al. proposed a rolling bearing fault diagnosis method combining a power spectrum analysis and support vector machine (SVM).