# Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism
## 5 Conclusions  
In conclusion, the multi-modal fusion algorithm proposed in this paper, which combines convolutional networks with self-attention mechanisms, provides an efficient and precise solution for the intelligent fault diagnosis of rotating machinery. The experimental results demonstrate that our method achieved an impressive classification accuracy of 97.5%, outperforming other mainstream methods, such as ViT, Swin Transformer, and ConvNeXt. In addition to its accuracy, our method has several advantages: it enhances the adaptability to complex operating conditions, exhibits strong robustness, and performs well across various fault types, ensuring reliable fault diagnosis under diverse scenarios. Additionally, the introduction of the Channel-Based Multi-Head Attention (CBMA) mechanism effectively leverages the complementary information from vibration and current signals, improving the fusion of multi-modal features.  
However, there are some potential limitations to consider. The method's performance may be influenced by the quality and consistency of the input data, and the increased complexity of the model may lead to increased computational costs in certain real-time applications. Despite these challenges, the proposed approach presents a significant advancement in the field of fault diagnosis and opens up new directions for future research. With further  
Figure 10: Comparison of Classification results of CBMA-ViT, ViT, Swin Transformer, and ConvNeXt.  
refinement, this method holds promise for broader applications in intelligent fault diagnosis systems, particularly in industrial environments with diverse operating conditions.  
Future research will focus on several key areas. First, we plan to optimize the method used for real-time applications by improving its computational efficiency and reducing the model's complexity. Second, we aim to enhance the robustness of the model by investigating strategies to handle noisy or incomplete data. Additionally, exploring the fusion of other data modalities, such as temperature and acoustic signals, will be a promising avenue to expand the model's diagnostic capabilities. Finally, further refinements to the attention mechanisms, such as introducing more advanced attention strategies, could lead to improved performance in complex fault diagnosis tasks. With these advancements, the method holds promise for broader applications in intelligent fault diagnosis systems, particularly in industrial environments with diverse operating conditions.  
Investigation, X.D.; Resources, S.Z.; Writing--original draft, H.H.; Writing--review & editing, J.Y.; Supervision, G.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under Grant 2022 YFB4703703, the Theory and Method of Excavation-Support-Anchor Parallel Control for Intelligent Excavation Complex System under Grant 52104169, and the Green, Intelligent, and Safe Mining of Coal Resources under Grant 52121003.  
Not applicable.  
Data Availability Statement: Data are unavailable due to privacy restrictions. No public involvement in any aspect of this research. No specific reporting guidelines were used in drafting this manuscript. AI or AI-assisted tools were not used in drafting any aspect of this manuscript.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.