# Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning
## 3 Methods
### Overview of the Proposed Model's Architecture
To further analyze our proposed approach, we construct confusion matrices to detail the classification performance for each task on the CWRU and JNU datasets, as illustrated in Figures 7 and 8. Confusion matrices provide an intuitive reflection of the model's classification effectiveness. By visualizing the number of true positives, false positives, true negatives, and false negatives for different categories, we can comprehensively assess the performance of the algorithm. Each row of the confusion matrix represents an actual category, while each column represents the category predicted by the model. The elements on the diagonal of the matrix indicate the probabilities of correct classification by the model, whereas the off-diagonal elements represent the probabilities of misclassification. For instance, in the C01 task of the CWRU dataset (with target domain categories 0, 1, 2, 3, 4, 7, 8, 9), the values 'nan' for true labels 5 and 6 indicate the absence of these categories. If the true label is 1 and the predicted label is 3, a corresponding value of 0.01 signifies that there is a 1% probability of category 1 being misclassified as category 3. Finally, we also employ the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to visualize the performance of our model on different tasks across two datasets, as illustrated in Figures 9 and 10. t-SNE is a dimensionality reduction technique, which we use to map high-dimensional features into a two-dimensional space, allowing for a more intuitive observation of the similarities and differences between samples. Through t-SNE visualization, we are able to observe the clustering of data points in a low-dimensional space. Taking the J03 task on the JNU dataset as an example, it is evident that category 1 was mistakenly classified as category 0, and category 3 was prone to being misclassified as category 1 and category 2. These visualizations also clearly show whether the features learned by the model enable samples of the same category to cluster more closely together, and whether there are clear boundaries between different categories on the t-SNE plot. Therefore, these visualization results provide us with an intuitive understanding of the model's performance and the effectiveness of its feature learning, further supporting our evaluation of the algorithm. Figure 7: Confusion matrix diagrams for each task on the CWRU dataset using the proposed method. Figure 8: Confusion matrix diagrams for each task on the JNU dataset using the proposed method. Figure 9: t-SNE plots for each task on the CWRU dataset using the proposed method. ## 5 Conclusions

This study introduces an innovative multi-scale bearing fault diagnosis method based on transfer learning, aimed at resolving the challenge of identifying unknown fault types in industrial equipment, thereby enhancing the accuracy of fault diagnosis and the reliability of the equipment. The proposed approach involves constructing MBDCNet and incorporating dynamic convolution, which effectively captures crucial information from bearing vibration signals and adapts to spatiotemporal variations in the input data. Furthermore, the use of pre-training and transfer learning strategies not only enables the model to learn more general vibration features in the source domain but also balances the information between the source and target domains through an improved loss function and fine-tuning of the learning rate, significantly enhancing the model's robustness and generalization capability. Experimental results on the bearing datasets from Case Western Reserve University and Jiangnan University demonstrate high diagnostic accuracy under various working conditions, achieving the highest average accuracies of 92.8% and 90.2%, respectively. The effectiveness of the method is further confirmed by the confusion matrix and t-SNE visualization results.