# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 4 Experimental Setup and Results
### Private Datasets  
We used our own experimental platform to further verify the proposed method, as shown in Figure 8. The main components include motor, driving band, shaft coupling, rotor  
Figure 6: Feature visualization via t-SNE in output layers for open datasets.  
Figure 7: The confusion matrix of the DAN-DAM model in two domains for open datasets.  
accuracy of the model stable at about 97%, so the model has strong stability and generalization and the diagnostic effect is good. To more fully analyze the stability of the model, the results of five consecutive diagnoses for each transfer condition are presented, as shown in Figure 10. According to the analysis in Figure 10, for the accuracy results of five consecutive outputs of each transfer case, the accuracy fluctuation is relatively small. The vast majority of transfer cases fluctuates within 2%, while a few transfer cases fluctuates slightly large, but is also basically maintained within the range from 2% to 4%. Therefore, the DAN-DAM model is relatively stable under the same transfer condition.  
The output layer of the DAN-DAM model and the DAN-DAM (no MMD) model are also features visualized by t-SNE on our own datasets. V3- > V2 is used as an example. Figure 11 shows the visualization result of t-SNE. It can be seen from the comparison between the two models that the number of overlaps between samples of different types of the DAN-DAM model is small and the number of sample misjudgments is low. Therefore, the diagnostic accuracy of the DAN-DAM model is better than that of the DAN-DAM (no MMD) model. In addition, in terms of the clustering effect, most samples of the same type in the DAN-DAM model are clustered into a cluster and only a small number of samples is not clustered with what sample types they belong, such as C2 and C3, while, in the DAN-DAM (no MMD) model, the sample gathering effect is poor and all samples of the same type are separated into multiple parts, for example, C4 has four parts. In conclusion, the addition of MMD to the model can improve the classification effect of the model, thus improving the diagnostic accuracy of the model and also, once again, verifying the function of MMD in the DAN-DAM model.  
Figure 11: Feature visualization via t-SNE in output layers for private datasets.  
Figure 10: The five consecutive test results of the DAN-DAM model in diverse transfers for private datasets.  
In order to more fully and specifically verify the classification ability of the proposed method, the confusion matrix diagram of the DAN-DAM model is also drawn on its own dataset, as shown in Figure 12. The overall classification ability is summarized as follows: The DAN-DAM model has achieved good classification results on its own datasets and all types of samples in the source domain can be classified 100% correctly. Except for the C2 samples on the target domain, 100% are correctly classified. In C2, 2% of the samples are misjudged as C3 and 3% of the samples are misjudged as C4. Therefore, the total number of misjudgment samples on the target domain is 50 and the total number of correctly classified samples is 3950. In summary, the fault diagnosis accuracy of the DAN-DAM model for the unlabeled target domain is 98.75%, which, once again, verifies the superior classification effect of the DAN-DAM model.