# Domain Generalization Model of Deep Convolutional Networks Based on SAND-Mask
## 3 Results
### Experiment and Analysis  
3.2.1. Experimental Comparison with Other Generalization Methods in Recent Years  
In recent years, there have been a number of approaches for domain generalization, including IB_IRM [32] (Information bottleneck--invariant risk minimization) and IB_ERM [32] (information bottleneck--empirical risk minimization), IRM [33] (invariant risk minimization), Fishr [34] (a new regularization named Fishr), and RSC [35] (representation). By comparing the DCNG model proposed in this paper with other generalization methods, the effectiveness of DCNG model compared with other generalization methods in the field of fault diagnosis is illustrated. 1. Compared with other generalization methods on the CWRU data set  
Figure 4 shows the results of DCNG on 24 transfer tasks in the CWRU dataset. It can be seen from the figure that the highest classification result is 98.65% on task 23 and the lowest classification result is 92.4% on task 13, with a 6.25% difference between them. NCNG performed above 92% on all transfer tasks. The performance of different generalization methods is analyzed from the perspective of a single task. As shown in Figure 5, the transfer performance of IB_IRM, IB_ERM, and IRM methods is lower than that of Fishr, RSC, and DCNG methods on a single task. For RSC and DCNG methods, the overall transfer result of RSC is higher than that of DCNG. However, in some transfer tasks, such as task 17, the performance of Fishr is lower than that  
Figure 4: Generalization performance of DCNG on CWRU data sets. of theDCNG method, and in the overall transfer tasks, the performance of Fishr is better than that of the DCNG model. The specific significance of the tasks is shown in Table 2.  
The performance of different generalization methods was analyzed from the perspective of the entire task. IB_IRM, IB_ERM, and IRM methods fluctuated greatly in each transfer task, especially in the odd transfer task. The Fishr method was the most stable and superior for the DCNG model. Although RSC performs better than the DCNG model on some transfer tasks, the results varied greatly between tasks and are not as stable as the DCNG model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Transfer Task** & **Source Domain** & **Target Domain** \\ \hline task 1 & A, B & A \\ task 2 & A, B & B \\ task 3 & A, B & C \\ task 4 & A, B & D \\ task 5 & A, C & A \\ task 6 & A, C & B \\ task 7 & A, C & C \\ task 8 & A, C & D \\ task 9 & A, D & A \\ task 10 & A, D & B \\ task 11 & A, D & C \\ task 12 & A, D & D \\ task 13 & B, C & A \\ task 14 & B, C & B \\ task 15 & B, C & C \\ task 16 & B, C & D \\ task 17 & B, D & A \\ task 18 & B, D & B \\ task 19 & B, D & C \\ task 20 & B, D & D \\ task 21 & C, D & A \\ task 22 & C, D & B \\ task 23 & C, D & C \\ task 24 & C, D & D \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DCNG transfer tasks on CWRU data sets. Figure 5: Performance comparison of different generalization methods on CWRU data sets. Compared with other generalization methods on the MFPT data set  
Compared with the CWRU data set, there were 90 transfer tasks on the MFPT data set, and the MFPT data set had larger variations in working conditions, which was conducive for exploring the influence of a single condition on the generalization method in the scenario of changing working conditions. Table 3 shows the results of DCNG model on 90 transfer tasks of MFPT data set. The average result of DCNG model on the transfer task of condition with target domain N is 99.35%, and the lowest result is 94% on the transfer task I, K-\(>\) N. Although the DCNG model achieved good results on different transfer tasks. However, when the \"distance\" of working conditions increased, transfer performance fluctuated. As shown in Figure 6, the results of different generalization methods are almost the same for other tasks outside the target domain N; thus, only results of the condition transfer task with the target domain N are displayed.