## 3 Multi-Adversarial Joint Distribution Adaptation Network
### Feature Visualization  
In order to understand the impact of different models more intuitively on sample features, the R0-R3 transfer task is selected for feature visualization. In this study, the t-distributed Stochastic Neighbor Embedding (t-SNE) technique was employed to reduce the dimensionality of sample features to 2D for visualization. t-SNE constructs two probability distributions expressing the similarity between sample points in high-dimensional and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Health** & **Fault Size** & **Label** \\
**Condition** & & \\ \hline Outer race fault & 7 mils & O-7 \\  & 14 mils & O-14 \\  & 21 mils & O-21 \\ \hline Inner race fault & 7 mils & I-7 \\  & 14 mils & I-14 \\  & 21 mils & I-21 \\ \hline  & 7 mils & B-7 \\ Ball fault & 14 mils & B-14 \\  & 21 mils & B-21 \\ \hline Normal & & N \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fault status information from CWRU dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **1D CNN** & **DANN** & **CORAL** & **DSAN** & **MAJDAN** \\ \hline R0-R1 & 94.63\% & 99.75\% & 96.75\% & **100\%** & 99.87\% \\ \hline R0-R2 & 85.44\% & **100\%** & 88.66\% & **100\%** & **100\%** \\ \hline R0-R3 & 77.50\% & 78.25\% & 78.25\% & **99.87\%** & **99.87\%** \\ \hline R1-R2 & 97.25\% & 99.75\% & 97.50\% & 99.25\% & **100\%** \\ \hline R1-R3 & 71.75\% & 99.75\% & 71.83\% & **99.75\%** & **99.75\%** \\ \hline R2-R3 & 94.75\% & 99.75\% & 94.25\% & **99.75\%** & **99.75\%** \\ \hline R1-R0 & 85.50\% & 97.42\% & 98.25\% & 99.00\% & **99.66\%** \\ \hline R2-R0 & 92.75\% & 87.38\% & 87.87\% & **99.31\%** & 98.25\% \\ \hline R3-R0 & 83.20\% & 78.55\% & 63.25\% & **97.25\%** & 95.75\% \\ \hline R2-R1 & 93.25\% & 93.76\% & 87.00\% & 95.44\% & **97.75\%** \\ \hline R3-R1 & 76.75\% & 77.25\% & 77.50\% & 96.25\% & **96.87\%** \\ \hline R3-R2 & 92.00\% & 96.66\% & 96.75\% & 98.00\% & **99.87\%** \\ \hline Average & 87.06\% & 92.36\% & 86.49\% & 98.66\% & **98.95\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results of CWRU dataset.  
low-dimensional space, respectively. It uses gradient descent to learn and optimize these distributions, aiming to make them as similar as possible. This ensures that the distance relationships between data points are preserved after dimensionality reduction. Figure 13 shows the graphical representation of untrained raw data features and features after training with different models following dimensionality reduction. It is observed that the source domain samples in (b)-(f) all achieve excellent classification results, which also proves the effectiveness of the feature extractor and label predictor. In Figure 13b, the 1 DCNN model undergoes no transfer training, leading to significant dissimilarity between the source domain and target domain sample distributions. Consequently, this mismatch results in poor diagnostic performance. The source and target domain sample features in (a) and (d) are also not well matched, while the features of the two domains in (e) and (f) are very close. But in some categories, feature clusters of (f) appear to be tighter and better matched than (e).  
Figure 13: t-SNE feature visualization. (**a**) Features without training. (**b**) 1 DCNN. (**c**) DANN. (**d**) CORAL. (**e**) DSAN. (**f**) MAJDAN.