## 4 Experiment and Validation
### Cross-Domain Fault Diagnosis Case 1: CWRUi
#### 4.2.2 Analysis of Results  
To compare the diagnosis results of the proposed method with those of VGGNet, Conv5, and ProtoNet, we take the example of 10-way classification from four datasets (Dataset A, Dataset B, Dataset C, Dataset D) generated in CWRU dataset. Figure 8 displays the results based on the CWRU dataset. Our proposed method achieves the best fault diagnostic performance on the CWRU dataset, regardless of the sample size used for training. Among them, when using 10-shot, the accuracy of our proposed algorithm on \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dataset Name** & \multicolumn{2}{c}{**Sample Category**} & **Sample Number** \\ \hline Source & \multirow{2}{*}{MinilmageNet} & Train & 100 & 100 \(\times\) 480 \\ domain & & Val & 100 & 100 \(\times\) 120 \\ \hline \multirow{4}{*}{Target domain} & \multirow{2}{*}{Dataset A} & Train set & 10 & 10 \(\times\) N (N = 3/5/10) \\  & & Test set & 10 & 10 \(\times\) (200-N) (N = 3/5/10) \\ \cline{1-1}  & \multirow{2}{*}{Dataset B} & Train set & 10 & 10 \(\times\) N (N = 3/5/10) \\ \cline{1-1}  & & Test set & 10 & 10 \(\times\) (200-N) (N = 3/5/10) \\ \cline{1-1}  & \multirow{2}{*}{Dataset C} & Train set & 10 & 10 \(\times\) N (N = 3/5/10) \\ \cline{1-1}  & & Test set & 10 & 10 \(\times\) (200-N) (N = 3/5/10) \\ \cline{1-1}  & \multirow{2}{*}{Dataset D} & Train set & 10 & 10 \(\times\) N (N = 3/5/10) \\ \cline{1-1}  & & Test set & 10 & 10 \(\times\) (200-N) (N = 3/5/10) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the datasets. Figure 6: Converted images on normal conditions. Figure 7: Converted images on nine fault conditions. the four datasets generated by CWRU Dataset A, Dataset B, Dataset C, and Dataset D is 95.22 \(\pm\) 0.28%, 98.12 \(\pm\) 0.21%, 96.16 \(\pm\) 0.23%, and 94.55 \(\pm\) 0.34%, respectively, with the mean value of the four datasets reaching achieved 96.01%. Its mean results are 13.80% and 8.78% greater than those of VGGNet and Conv5, respectively, and our approach improves by 3.51% compared to the best few-shot learning ProtoNet. By comparing the results for training samples of 3-shot and 5-shot, it is clear that our method performs better than other metcots when less training data are used. The fault diagnostic accuracy of the deep learning-based technique is lower than that of the few-shot learning method when compared to the few-shot learning method. This is because the limited amount of data used here could lead to overfitting because traditional deep learning methods need a due amount of training data. Changes in working conditions may also affect the performance of these deep learning systems due to their weak generalization capacity. The experimental results show the adaptability of our proposed cross-domain few-shot learning method. To illustrate the proposed method classification accuracy and error ratio for the bearings under ten operational scenarios, Figure 9 displays the confusion matrix for the various methods, using an example for each task from the Dataset B dataset for the 10-shot condition. The accuracy rate of each bearing health status is shown in the right column of the confusion matrix, and the recall rate is shown in the bottom row. As illustrated in Figure 9, the proposed method effectively determines the health status of rolling bearings under various operating circumstances. The model performs admirably in each of the ten working conditions. Additionally, it is evident that the NC category has a 100% recognition rate. This indicates that even if the diagnostic errors of the proposed method are primarily caused by incorrect estimation of BF and IF defects, bearing failures can still be identified. In partic  
Figure 8: Identification accuracy (%) of recognition under different methods. ular, this proposed method ensures that healthy and fault bearings are not misclassified, preventing incorrect information from being delivered in practical applications.