## 3 Experiments
### Transfer Results
#### 3.2.3 Results of Transfer Tasks at CWRU  
For the unsupervised domain adaptation problem described above, this study trains RASAN-W utilizing labeled data from the source domain and unlabeled data from the target domain. To minimize the influence of randomness and to assess the robustness of the model, each domain adaptation task is conducted ten times, and the average accuracy is presented in Table 3. The results indicate that RASAN-W consistently maintains the highest accuracy in the majority of the transfer tasks, with an average accuracy of 99.4%. ResNet exhibits the worst bearing fault diagnosis accuracy, with an average accuracy of 73.5%. In addition, for DDC (87.7%), DANN (92.7%), DSAN (97.9%), DASAN (94.2%), and RASAN-W (99.2%) and several other domain-adaptive-based methods, the accuracy of bearing fault diagnosis is generally higher than that of the baseline method, which reflects the superior performance of the domain adaptive method when the sample distribution between domains does not conform to an independent homogeneous distribution. Meanwhile, DSAN (97.9%), DASAN (94.2%), and RASAN-W (99.2%) based on subdomain adaptation have obvious advantages over DDC (87.7%) and DANN (92.7%), which ignore the category information, proving the necessity of considering the category information in domain adaptation. In addition, in order to visually analyze and compare the diagnostic performance of each method, the output features of the feature extractor are downscaled to two dimensions using the t-distribution stochastic neighbor embedding (t-SNE) method, and the feature visualization results of multiple methods are shown in Figure 5. From Figure 5a, it can be seen that ResNet not only fails to cluster the same category of sample features in the dataset, but also there is obvious confusion in the separation of different categories of sample features, and in Figure 5c, the same category of sample features can be well clustered, but the separation between different categories of sample features fails, and a large number of features are confused, and these lead to the inability to distinguish the feature categories, resulting in bearing fault diagnosis errors. In Figure 5b,d,e, different category features can be well separated, but the clustering of the same category samples is poor, which reduces the bearing fault diagnosis accuracy. In Figure 5f, it is easy to see that compared with the current mainstream domain adaptive methods, the proposed RASAN-W effectively aligns the global and local feature distributions, which makes the sample features have better interclass differentiation and intraclass tightness, and reduces the differences in the sample distributions that exist between the domains, which effectively improves the diagnostic accuracy of RASAN-W in the bearing fault diagnosis process. Taking the task (\(C_{0}\to C_{2}\)) as an example, observing the specific errors of fault recognition, the confusion matrix of the experimental results is shown in Figure 6a.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & \(C_{0}\to C_{1}\) & \(C_{0}\to C_{2}\) & \(C_{0}\to C_{3}\) & \(C_{1}\to C_{2}\) & \(C_{2}\to C_{3}\) & \(C_{3}\to C_{0}\) & **Average** \\ \hline ResNet & 0.649 & 0.735 & 0.740 & 0.745 & 0.691 & 0.713 & 0.712 \\ DDC & 0.908 & 0.847 & 0.834 & 0.933 & 0.809 & 0.928 & 0.877 \\ DANN & 0.926 & 0.921 & 0.968 & 0.948 & 0.851 & 0.946 & 0.927 \\ DSAN & 0.985 & 0.968 & 0.983 & 0.978 & 0.973 & 0.988 & 0.979 \\ DASAN & 0.993 & 0.965 & 0.896 & 0.980 & 0.851 & 0.965 & 0.942 \\ RASAN-W & 0.995 & 1.000 & 0.985 & 0.998 & 0.990 & 0.975 & 0.991 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of the six transfer tasks using different fault diagnosis methods on CWRU dataset (%).