## 4 Experiment and Validation
### Cross-Domain Fault Diagnosis Case 2: Production Line
#### 4.3.2 Analysis of Results  
The proposed method is still compared with VGGNet, Conv5, and ProtoNet in order to confirm the testing accuracy of the method, and the results are displayed in Figure 13. It is clear from the data in the figure that our proposed method, regardless of the amount of training samples used, may demonstrate a considerable improvement in fault diagnosis accuracy. Among them, the accuracy rates of VGGNet and Conv5 are 79.64 \(\pm\) 0.96% and 82.46 \(\pm\) 0.83%, respectively. Further investigation reveals that the substantial model overfitting issue and the short data sets that are insufficient for training are the primary causes of their subpar classification results, which severely restrict the performance of fault diagnosis. Despite considerable advancements, the fault diagnosis performance of the ProtoNet method is still subpar. The four methods are compared using the 10-shot as an example. Our method improves by 15.52%, 12.70%, and 2.18% compared to VGGNet, Conv5, and ProtoNet 10-shot, respectively. The experimental results demonstrate the high fault diagnosis performance of our proposed cross-domain few-shot learning method.  
For each task in the dataset, the confusion matrix of the fault diagnosis results for various method models was examined under 4-way 10-shot conditions, as illustrated in Figure 14, in order to evaluate the performance of the proposed method visually. The predicted and actual health statuses of the test sample are represented by the horizontal and vertical axes, respectively, in the confusion matrix. According to Figure 15, the total average detection accuracy of the experimental data set may reach 95.16 \(\pm\) 0.20%, and the classification accuracy of the production line bearings under the four working conditions achieves a more desirable result. The two fault kinds of IF and OF, which are often confused, are the principal manifestation of the accuracy difference. The method in this paper has a strong diagnostic performance for the health state of NC, and this shows that the method in this paper has a low risk of false alarms. Overall, the method that is proposed in this  
Figure 13: Accuracy (%) of identification of other models compared with the proposed model.  
paper has a high fault diagnosis accuracy and is adequate to meet the actual requirements of the sector.  
Figure 14: The confusion matrix of fault diagnosis results under different methods. (**a–d**) correspond to the VGGNet, Conv5, ProtoNet-10 shot, and our method-10 shot.  
Figure 15: Visualization of engineering data with different methods for t-SNE. (**a–d**) corresponding to the VGGNet, Conv5, ProtoNet-10 shot, and our method-10 shot.  
The t-SNE algorithm is used to visualize the stream structure of the original samples by projecting each high-dimensional sample onto a data point on a 2D plane. This helps to further evaluate the performance of the proposed method visually. The output similarity vector created by the method presented in this study is applied to the t-SNE algorithm in the analysis, and the final visualization results are displayed in Figure 15. Overall, the proposed method can successfully increase fault identification accuracy. It is clear that data points with similar bearing health statuses are highly grouped together, but data points with various failure categories are well-segregated. As a result, it can be used to classify bearing faults on a production line.