## 4 Experiment and Analysis
### The Experimental Contrast  
In this paper, four widely cited methods were selected for comparative experiments. These included deep adaptation network (DAN) [29], dynamic adversarial adaption network (DAAN) [30], multi-feature representation adaption network (MRAN) [31], and deep subdomain adaptation network methods with ResNet-50 as a feature extractor (RDSAN).  
The input of the selected comparison method is a time-frequency image with a pixel value of 246 px \(\times\) 246 px. The same feature extractor (ResNeSt-50) is used for DAN, DAAN, and MRAN to ensure that the experimental comparison would be valid. The experimental results are shown in Table 3. The average diagnostic accuracy of the proposed method in 12 migration tasks is 99.9%, which is higher than the accuracy obtained using other diagnostic models.  
Combining Table 3 and Figure 7 shows that the DAN adopts global alignment, with an average diagnostic accuracy of 91.5%. However, the diagnostic accuracy of each task fluctuates greatly, especially for task 2-0 (only 88%). The reason for such behavior is that global adaptation aims to align the overall distribution of the \(D_{s}\) and the \(D_{t}\); thus, the correlation between each subfield is ignored. The DAAN has the weakest recognition effect, with an average diagnostic accuracy of 91.6%. The recognition accuracy fluctuates greatly, and its robustness is the lowest. This is due to the global alignment that is based on adversarial thinking, requiring a large sample set of \(D_{s}\) and \(D_{t}\) to confuse the domain discriminator, making it unable to judge the sample domain label to achieve the global alignment. Therefore, using too few samples is the main reason for its low diagnostic accuracy.  
The MRAN makes up for the DAN defect by extracting multi-representation features of sample images and aligning them in different feature spaces. However, the diagnostic accuracy in task 1-0 is only 94%. The primary reason is that when the samples of the \(D_{s}\) and the \(D_{t}\) have high similarity, the extracted multi-representation features are more similar. Hence, they cannot be classified correctly, yielding an average diagnostic accuracy of 98.8%.  
Under the premise of using the subdomain migration method, the average diagnostic accuracy of RDSAN using Resnet-50 as a feature extractor is 98.8%. This value is lower than 99.9% obtained for the SPDSAN, proving that the SPDSAN has a stronger ability to learn fault features.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Healthy State** & **Fault Size** & **0 HP** & **1 HP** & **2 HP** & **3 HP** & **All** \\ \hline NA & 0 & 486 & 966 & 968 & 968 & 3388 \\ \hline IF & 0.1778 & 241 & 242 & 243 & 244 & 970 \\  & 0.3556 & 242 & 242 & 242 & 242 & 968 \\  & 0.5334 & 243 & 242 & 242 & 242 & 969 \\ \hline OF & 0.1778 & 242 & 243 & 241 & 244 & 970 \\  & 0.3556 & 242 & 242 & 242 & 242 & 968 \\  & 0.5334 & 243 & 242 & 243 & 242 & 970 \\ \hline BF & 0.1778 & 244 & 241 & 242 & 242 & 969 \\  & 0.3556 & 242 & 243 & 242 & 243 & 970 \\  & 0.5334 & 242 & 242 & 243 & 243 & 970 \\ \hline All & — & 2667 & 3145 & 3148 & 3152 & 12,112 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The CWRU sample size scale.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Methods/Task** & **0-1** & **0-2** & **0-3** & **1-0** & **1-2** & **1-3** & **2-0** & **2-1** & **2-3** & **3-0** & **3-1** & **3-2** & **Average** \\ \hline DAN & 95.2 & 99.0 & 95.6 & 90.2 & 96.6 & 96.3 & 88.0 & 93.4 & 98.8 & 84.0 & 90.3 & 91.5 & 93.2 \\ DAAN & 98.0 & 97.0 & 92.0 & 89.0 & 99.0 & 96.0 & 82.0 & 93.0 & 89.0 & 85.0 & 82.0 & 97.0 & 91.6 \\ MRAN & 99.0 & 100 & 99.0 & 94.0 & 100 & 99.0 & 100 & 99.0 & 100 & 97.0 & 99.0 & 100 & 98.8 \\ RDSAN & 99.5 & 99.9 & 99.8 & 99.7 & 100 & 99.9 & 99.7 & 99.3 & **100** & 90.5 & 99.2 & 100 & 98.9 \\ SPDSAN & **100** & **100** & **99.9** & **99.9** & **100** & **99.9** & **99.4** & 99.9 & **99.5** & **99.8** & **100** & **99.9** \\ \hline \end{tabular}
\end{table}
Table 3: Diagnostic accuracy of different models (%).