# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 4 Discussion
### Visualization Analysis  
We performed Visual analysis of the model with confusion matrix and t-SNE [39] distribution. First, perform a visual analysis with the confusion matrix. We choose the composite model's 0\(\rightarrow\)2 cross-domain task with relatively lower accuracy for display. The visualization matrix of the results of applying the composite model to the test dataset from working condition 2 is shown in Figure 11a. It can be seen from the figure that the classification results of the composite model on the B021 fault have serious errors, and nearly 40% of the B021 faults are misclassified as B007 faults. Besides, 5.29% and 3.95% of the samples from the B007 fault and B014 fault were incorrectly classified as B021 fault, respectively, indicating that the composite model did not well distinguish the feature distributions corresponding to several fault diameters on the rolling ball. On the IR014 fault, 9.43% of the samples were incorrectly classified as the OR021 fault, indicating that the composite model could not completely classify the IR014 fault effectively. We believe that this is due to the phenomenon of domain mismatch because in this transfer, most of the classifications have achieved good results while only a few classes are seriously misclassified. The result indicates that the model incorrectly matches some B021 data of the target domain with the B007 data of the source domain, and calculates and reduces the field distribution between the two, so that this part of the B021 data is incorrectly classified as B007 data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Working Conditions**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-5}  & **0** & **1** & **2** & **3** \\ \hline MK-MMD+DANN & 93.15 & 96.02 & 92.16 & 96.87 & 94.55 \\ Dual source domains MCDANN & 94.67 & 98.17 & 94.54 & 98.35 & 96.48 \\ Three source domains MCDANN & **96.17** & **98.56** & **97.74** & **99.23** & **97.93** \\ \hline \hline \end{tabular} Note: The bold number mean it is the largest in this column.  
\end{table} 
Table 6: Classification accuracy of cross-domain models with different number of source domains under cross-working conditions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Cross-Working Conditon**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-5}  & **0\(\rightarrow\)1** & **0\(\rightarrow\)2** & **0\(\rightarrow\)3** & **1\(\rightarrow\)2** & **1\(\rightarrow\)3** & **2\(\rightarrow\)3** \\ \hline DANN & 95 & 88.86 & 90.11 & 89.87 & 88.41 & **97.15** & 91.57 \\ DANN+MMD & **96.31** & 90.17 & 93.22 & 91.24 & 88.33 & 96.34 & 92.6 \\ DANN+MK-MMD & 96.02 & **91.13** & **95.91** & **92.16** & **88.52** & 96.81 & **93.43** \\ \hline \hline \end{tabular} Note: The bold number mean it is the largest in this column.  
\end{table} 
Table 5: Classification accuracy of models using distance for domain adaptation in cross-working conditions. Then we show the confusion matrix of the results of applying MCDANN on the test set of working condition 2, as shown in Figure (b)b. The classification accuracy of the MCDANN method on ball faults is generally low, and there are a few misclassifications between IR014 and OR021. However, although the misclassification problem of domain adaptation on ball faults cannot be completely solved, the MCDANN method still achieves a great improvement in this. On the B021 fault whose originally classification accuracy is 61.15%, the accuracy was increased to 92.55%, with an increase of more than 30%. Originally only 93.75% of B007 faults have been increased to 97.8%. The classification accuracy of the B007 fault is improved from 93.75% to 97.8% while the classification accuracy of B014 faults just has a slight improvement, from 95.39% to 95.97%. Then the features obtained by these methods in the domain are visualized with t-SNE distribution, with working condition 0 as the source domain and working condition 2 as the target domain.