# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 3 Experiments
### Experiment Result
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Cross-Working Condition**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-7}  & **0\(\rightarrow\)1** & **0\(\rightarrow\)2** & **0\(\rightarrow\)3** & **1\(\rightarrow\)2** & **1\(\rightarrow\)3** & **2\(\rightarrow\)3** & \\ \hline CNN & 91.59 & 81.82 & 85.68 & 86.02 & 83.52 & 93.86 & 87.08 \\ DAN & 90.34 & 85.57 & 85.57 & 87.27 & 83.41 & 96.36 & 88.09 \\ DSAN & 85.11 & 78.23 & 80.34 & 83.41 & 78.52 & 91.47 & 87.08 \\ DANN & 95.00 & 88.86 & 90.11 & 89.87 & 88.41 & **97.15** & 91.57 \\ 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 2: Cross-domain classification accuracy of different models. of conversary, the composite model combined with the MK-MMD distance can further improve the cross-domain accuracy by 1% to 5%, indicating that the composite model combining these two methods can achieve a good classification result. Figure 8 shows the accuracy result of the baseline CNN model, the feature-based DAN model, the adversarial-based DANN model, and the composite model. It can be seen from Figure 8 that the composite model has high diagnosis accuracy on three cross-domain tasks of 0\(\rightarrow\)1, 0\(\rightarrow\)3, 2\(\rightarrow\)3, all above 95%. On the cross-domain tasks of 0\(\rightarrow\)2, 1\(\rightarrow\)2, 1\(\rightarrow\)3, The diagnosis accuracy is slightly lower, all around 90%. Compared with other methods, the composite model has the greatest improvement on the 0\(\rightarrow\)3 cross-domain task, while the improvement in other cross-domain tasks is a bit smaller. Although the composite model can improve the accuracy of diagnosis on various cross-domain tasks, the disadvantage of combining the two methods is that the model becomes bloated, and the loss function is complicated, resulting in a reduced training speed. This is where the composite model falls short. Then this paper conducts multi-source domain adaptation experiments. The multi-source methods compared with the MCDANN method proposed in this paper fall into two categories. The first is the single-source domain optimal method. For each source domain, we use this method to operate a singe-source cross-domain test on the target domain and get the best accuracy as the benchmark accuracy. The second is the multi-source methods which are extended to single-source domain methods. Among the feature-based methods, the DAN method is selected for multi-source domain expansion, and the MK-MMD distance between each source domain and target domain is calculated and the weighted sum is performed as the domain loss function for training; Among the adversarial-based methods, the DANN method is selected for expansion, computing the adversarial loss between each source and a target domain and weighted summation as the domain loss function for training. The multi-source methods all take three source domains to participate in the training, and the comparison results of several methods are shown in Table 3.  
Table 3 indicates that among the single-source domain methods, the composite model still has the best performance. Compared with the single-source domain, the DAN method and the DANN method using multi-source extension have a considerable improvement, which proves that multiple source domains can indeed provide more information to correct the problem of accuracy drop in cross-domain. For the MCDANN method proposed in this paper, a consistency regularization loss is introduced besides the domain loss and classification loss, which improves the accuracy of the model by reducing the prediction difference on the target domain by models from several source domains. Therefore, we can get cross-domain diagnosis results in various domains with an accuracy of more than 96%, which proves its effectiveness. Figure 8: Classification accuracy of each method under cross-working conditions.