# Domain Generalization Model of Deep Convolutional Networks Based on SAND-Mask
## 3 Results
### Experiment and Analysis
#### 3.2.3 Compared with the Model without SAND Mask  
1. Compare the generalized model DCNG and the ungeneralized model on the CWRU data set  
Figure 12 shows the comparison between the generalized model DCNG and the ungeneralized model on the CWRU data set, where the ungeneralized model represents the same network structure as the model DCNG but without generalization measure SANDMask. The average transfer result of the ungeneralized model on the entire transfer task is 97.43%, the highest transfer result on task 7 is 99.69%, and the lowest transfer result on task 10 is 93.04%. The average transfer result of the generalized model DCNG on the overall transfer task was 95.91%, the highest transfer result on task 23 was 98.65%, and the lowest transfer result on task 13 was 92.4%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Settings** & \(\mathbf{\tau}\) & \(\mathbf{\rho}\) \\ \hline Parameter 1 & 0.6 & 100.0 \\ Parameter 2 & 0.6 & 500.0 \\ Parameter 3 & 0.6 & 1000.0 \\ Parameter 4 & 0.65 & 100.0 \\ Parameter 5 & 0.7 & 100.0 \\ Parameter 6 & 0.8 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Different parameter settings on KAT data set. Figure 11: The influence of different parameter settings on DCNG model in KAT data set. The average transfer performance of the ungeneralized model is 1.52% higher than that of the DCNG model, indicating that the smaller \"distance\" between the working conditions is easier to generalize in the scenario where the single condition causes a change in working conditions and the DCNG model has a good generalization effect on the CWRU data set. 2. Compare the generalized model DCNG and the ungeneralized model on the MFPT data set The DCNG model without generalization measures was compared with the generalized model, and the results of the DCNG model without generalization measures on the MFPT dataset are shown in Table 8.  
Figure 13 shows the comparison between the results of the generalized model DCNG and the ungeneralized model on 15 transfer tasks with N working conditions as the target domain in the MFPT dataset. The results of the ungeneralized model on the transfer task with N working conditions as the target domain are quite different from those of the DCNG model, so we only compare the results on the migration task with N working conditions as the target domain. The average result of the ungeneralized model on 15 transfer tasks is 96.98%, while the average result of the generalized model on 15 transfer tasks is 99.35%. Figure 12: DCNG and ungeneralized model performance on 24 tasks in the CWRU dataset. There is a 2.37% difference between the average transfer results of the generalized model and the ungeneralized model. Relatively speaking, the performance of the ungeneralized model fluctuates greatly in the transfer task with N conditions as the target domain, and the model is greatly affected by the \"distance\" of conditions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SourceTarget** & **I** & **J** & **K** & **L** & **M** & **N** \\ \hline L, J & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 90.24 \\ I, K & 100.00 & 100.00 & 100.00 & 99.75 & 100.00 & 94.74 \\ I, L & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 92.24 \\ I, M & 100.00 & 100.00 & 100.00 & 99.75 & 100.00 & 97.75 \\ I, N & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ J, K & 100.00 & 100.00 & 100.00 & 99.75 & 100.00 & 93.25 \\ J, L & 99.50 & 100.00 & 100.00 & 100.00 & 100.00 & 99.25 \\ J, M & 88.24 & 97.24 & 97.24 & 100.00 & 100.00 & 95.74 \\ J, N & 100.00 & 100.00 & 100.00 & 99.75 & 100.00 & 100.00 \\ K, L & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 93.75 \\ K, M & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 98.50 \\ K, N & 99.50 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ L, M & 98.50 & 100.00 & 99.75 & 100.00 & 100.00 & 100.00 \\ L, N & 97.49 & 99.75 & 99.75 & 100.00 & 100.00 & 99.75 \\ M, N & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 99.50 \\ \hline \end{tabular}
\end{table} 
Table 8: Performance of ungeneralized model DCNG on MFPT dataset.