# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 4 Discussion
### Visualization Analysis
Their t-SNE distribution of the feature from the last layer of the network is shown in Figure 12.  
In this figure, the red part is the feature distribution of the target domain, that is, the distribution of the feature output of the last layer of the network on the test set of working condition 2. The gray part is the feature distribution of the source domain, that is, the output of the last layer of the network on the test set from working condition 0. The higher the degree of overlap between the two, the closer their domain distributions are. Since the  
Figure 11: Confusion matrix of single source model and multi-source model on working condition 2. (SNR = \(-\)4). (**a**) DANN+MK-MMD; (**b**) MCDANN. Figure 12: Different models’ domain distribution output of the test set from working condition 2. (SNR = \(-\)4). (**a**) CNN; (**b**) DANN+MK-MMD; (**c**) MCDANN. domain adaptation model is trained on the labeled source domain, the prediction of the target domain is made according to the source domain label, so the prediction of the target domain can be judged by the degree of overlap between the gray area and the red area. For the convenience of observation, the gray area is placed on the upper layer, so that the degree of overlap between the two can be judged by observing the coverage of the gray area to the red area, and then the transfer performance of the model is analyzed accordingly. Figure 12a is the result of the CNN model without any transfer method. It can be seen from the figure that the gray area is divided into 10 clusters, corresponding to the 10 fault modes in working condition 0. The distances between these gray clusters are not short, which indicates that the CNN model has successfully classified the data on the source domain. In the red area, there are only 8 clusters. In the upper right corner of the figure, we can see that there is a large red area covering two gray areas, indicating that two fault modes are confused together in the classification process of working condition 2, thus being classified into one cluster. On the right side of the figure, there is little overlap between the red area and the gray area, which indicates that there is a large distribution difference between the source and target domains on a specific fault mode. There is a gray area at the bottom of the figure with almost no red area overlapping with it, indicating that the CNN model hardly recognizes a certain fault in this working condition, so most samples of this fault are misclassified into other clusters. In other clusters, the gray area and the red area have some overlap, but they are not completely covered. Figure 12b is the result of the composite domain adaptation model that combines the DANN network and the MK-MMD distance. It can be seen that the overall coverage of gray to red has improved, and each gray area covers the red area, indicating that the composite model improves the recognition rate of faults in working condition 2, and faults can be successfully classified into 10 categories. However, there are still only 9 red clusters, and there is still a large red area connected together in the upper left corner, indicating that the two confused faults have not been effectively distinguished. Figure 12c is the result of the MCDANN model using a multi-source domain for domain adaptation. In the final feature output of the model, it can be seen that the gray coverage is quite high, and the red part that can be seen in the figure is the least. Not only the gray area can be divided into 10 clusters, but the red area can also be roughly divided into 10 clusters. Although there are two red areas on the left that are still connected, it is much better than the connected red areas in (a) and (b). This shows that the MCDANN model performs well on the unlabeled test set from working condition 2, which also proves the effectiveness of the method proposed in this paper.