# Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
## 4 Results  
Here this paper compares the performance of the IATN with CNN [61], DANN [62], AMDSA, DASN, ISAE-CSDF [63] and CDGATLN [64] models on the three datasets, and the results are shown in Tables 4-6. In addition, the experiment in this paper is repeated 20 times to eliminate the influence of randomness.  
One can generally make three observations: (1) the IATN has the highest accuracy on most tasks. (2) the IATN has the most minor variances. (3) The more similar the working conditions of the source and target domain data are, the better the transfer learning effect will be.  
In Table 4, the diagnosis accuracy of the IATN is significantly higher than the other six methods on most tasks. In the task of D0\(\rightarrow\)D3, all the methods have the lowest accuracy. The accuracies of the CNN, DANN, AMDSA, DASN, ISAE-CSDF and SA-SN-DCGAN are 58.75%, 61.82%, 65.28%, 60.32%, 65.64% and 66.82%, respectively, but the accuracy of the IATN is 70.62%. In the task of D0, D1, D3\(\rightarrow\)D2, the accuracies of the CNN, DANN, TL and DSAN are 95.12%, 96.16%, 95.44%, 93.66%, 96.08% and 96.06%, respectively, but the  
Figure 4: Dual-rotor test bench. (**a**) The test rig; (**b**) engineering sketch; (**c**) bearing failure mode.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **LR Speed (rpm)** & **HR Speed (rpm)** \\ \hline D0 & 2000 & 4000 \\ D1 & 3000 & 6000 \\ D2 & 4000 & 8000 \\ D3 & 5500 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The working conditions of dual-rotor test bench.  
accuracy of the IATN is 98.44%. In the last row, this paper calculates the average accuracy of the five methods on all tasks, and the average accuracy of the IATN is 91.89%, which is 6.82%, 4.77%, 1.06%, 2.52%, 2.58% and 1.23% higher than the CNN, DANN, ISAE-CSDF and SA-SN-DCGAN, respectively. As for variance, the average variance of the IATN is 1.10%, which is significantly lower than the averages of the other six methods, which are 2.29%, 1.72%, 1.52%, 1.91%, 1.35% and 1.49%, respectively.  
In Tables 5 and 6, one could also find similar discoveries by comparing the performance of the IATN and the other methods. The average accuracy of the IATN on all CWRU tasks is 97.74%, which is 4.38%, 3.22%, 1.48%, 1.95%, 2.19% and 1.93% higher than the CNN, DANN, AMDSA, DASN, ISAE-CSDF and SA-SN-DCGAN, respectively. The average variance of the IATN on all CWRU tasks is 0.47%, which is lower than the other six methods, which  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multicolumn{8}{c}{**Model Performance (\%)**} \\ \cline{2-9}  &  
are 0.52%, 0.75%, 0.96%, 1.07%, 0.64% and 0.72%, respectively. The average accuracy of the IATN on all Ottawa dataset tasks is 81.11%, which is 6.77%, 4.23%, 1.42%, 3.78%, 3.28% and 0.84% higher than the CNN, DANN, AMDSA, DASN, ISAE-CSDF and SA-SN-DCGAN, respectively. The average variance of the IATN on all Ottawa dataset tasks is 1.24%, which is lower than those of the other six methods, which are 1.56%, 1.97%, 2.10%, 2.38%, 1.28% and 1.34%, respectively.  
The comparison results demonstrate that the IATN significantly improves bearing fault diagnosis under variable WDs. Moreover, when the WDs of the source and target domains are similar, the test accuracy obtained is also higher. For example, the accuracy of all methods on the D2\(\rightarrow\)D3 task is higher than that on the D0\(\rightarrow\)D3 task. Data similarity may be higher when operating conditions are similar, making it easier to transfer fault diagnosis knowledge. This suggests that data similarity may be higher when WDs are similar, making it easier to transfer fault diagnosis knowledge.