## 4 Experimental Analysis and Verification
### Feature Visualization  
To further verify the fault classification effect of the proposed method in a bearing fault diagnosis, the t-distributed stochastic neighbor embedding [38] (t-SNE) was employed to reduce the dimensionality of the original data features, and the features were mapped \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(\mathbf{A\to E}\) & \(\mathbf{A\to F}\) & \(\mathbf{A\to G}\) & \(\mathbf{A\to H}\) &
\begin{tabular}{c} **Average** \\ **Accuracy** \\ \end{tabular} \\ \hline DBN & 83.75 & 72.292 & 70.417 & 71.042 & 74.375 \\ S-Alexnet & 84.167 & 73.333 & 72.5 & 71.25 & 75.313 \\ IG-CWT-CNN & 83.958 & 70.833 & 69.375 & 68.333 & 73.125 \\ TF-MDA & 86.667 & 73.75 & 71.25 & 72.083 & 75.938 \\ ICPW-HPF & 81.458 & 67.5 & 66.875 & 64.792 & 70.156 \\ ML & 75.208 & 61.042 & 62.292 & 57.5 & 64.01 \\ JDA & 86.458 & 80.417 & 79.167 & 77.5 & 80.886 \\ TCA-DBN & 93.542 & 91.875 & 90.625 & 90.833 & 91.719 \\ DSAN & 97.917 & 97.708 & 98.333 & 97.708 & 97.916 \\ ITCA-DBN & 99.375 & 99.167 & 98.333 & 98.75 & 98.906 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cross-type and cross-working condition fault diagnoses result from using different methods. Figure 7: Radar chart of cross types and cross-working conditions fault diagnoses results using different methods. using TCA, DSAN, and ITCA, respectively, taking the data of task A \(\rightarrow\) F across the types and across working conditions as the object. The t-SNE could reduce the high-dimensional features into two-dimensional features, which were plotted using scatter plots to describe the classification ability of the different methods. The detailed visualization results are displayed in Figure 8. In Figure 8, S-OF, S-IF, S-RF, and S-NO represent the outer ring fault, inner ring fault, rolling element fault, and normal state in the source domain, respectively. T-OF, T-IF, T-RF, and T-NO represent the outer ring fault, inner ring fault, rolling element fault, and normal state in the target domain, respectively. Figure 8a depicts the unprocessed original distribution of the sample features, and it can be observed that the four types of data in the source domain and the four types of data in the target domain were mixed and could not be distinguished. The distribution of the data within each individual domain, whether source or target, was highly distinguishable across the four categories. Figure 8b shows the feature distribution after TCA mapping. While the fault types in the figure are easier to distinguish, significant discrepancies persisted in the distribution of the same fault types between different domains, easily leading to confusion with other fault types. Figure 8c shows the feature distribution processed using DSAN. The distribution of the various types of data in the target domain was approximately aligned with the corresponding types of data in the source domain, resulting in a relatively clear decision boundary. Figure 8d shows the feature distribution after ITCA mapping, where various types of data in the target domain align with corresponding types of data in the source domain, featuring clear boundaries and easy distinction. From the results shown in Figure 8, we can notice that: (1) TCA that achieves domain adaptation can reduce the distribution discrepancies between different domains but the effect is inconspicuous. (2) DSAN that achieves subdomain adaptation can reduce the distribution discrepancies of the same fault types between different domains, rendering it easier to distinguish the  
Figure 8: Feature visualization results. (**a**) Original distribution of the sample features; (**b**) TCA; (**c**) DSAN; (**d**) ITCA. fault types of the samples. However, there still were edge samples that were confused with other types.