# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 4 Experiments
### Experimental Performance and Analysis
In the experiments on the JNU dataset, we focus on verifying the generalization ability of the proposed method for bearing fault diagnosis in the variable speed environment. It can be seen that in the vast majority of tasks, our proposed method still has the best effect, which fully proves the advancement of our method. Further analysis shows that the performance of our method on the last five tasks is better than for the first five tasks; the difference between the two types of tasks is mainly reflected in the difference between the speed change of the source domain and the target domain. The first five tasks are changed from a low speed in the source domain to a high speed in the target domain. The situation is opposite for the latter five tasks, so the bearing fault features in the target domain are enhanced compared with those in the source domain, which is more conducive to extracting the fault features. The performance of our proposed method is more stable while maintaining overall high accuracy, which confirms that our proposed method has good generalization ability in the variable speed environment. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task** & **DANN** & **BWAN** & **UDA** & **EVT-OSFD** & **Ours** \\ \hline C01 & 69.4 & 70.8 & 79.9 & 83.8 & 97.3 \\ C02 & 66.8 & 70.2 & 78.1 & 81.5 & 97.2 \\ C03 & 73.8 & 72.8 & 70.8 & 78.6 & 97.8 \\ C10 & 72.5 & 73.0 & 80.7 & 84.1 & 96.5 \\ C12 & 68.2 & 71.0 & 84.6 & 90.7 & 99.8 \\ C13 & 65.7 & 69.0 & 79.0 & 84.4 & 98.6 \\ C20 & 65.3 & 65.4 & 70.5 & 82.9 & 97.9 \\ C21 & 74.2 & 70.6 & 78.4 & 83.9 & 98.6 \\ C23 & 74.6 & 74.6 & 78.4 & 87.3 & 97.6 \\ C30 & 56.4 & 50.3 & 68.6 & 74.4 & 95.5 \\ C31 & 76.3 & 82.4 & 71.3 & 94.9 & 99.8 \\ C32 & 75.9 & 83.1 & 74.7 & 96.3 & 98.4 \\ \hline Avg & 69.9 & 71.1 & 77.3 & 85.2 & 97.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The classification accuracy (%) of the CWRU dataset. To prove the advancement and generalization ability of our proposed method, we verified it on the bearing test bed in the laboratory. Our method shows superior and stable performance. Compared with the JNU dataset, the speed variation is larger, but the accuracy fluctuation is smaller. First, our test bed's structure differs. Its drive shaft is directly connected to the motor, while the JNU test bed is indirectly driven, making our vibration signal more obvious. Second, differences in the selected bearings and man-made faults also affect fault characteristic performance. Overall, our model shows excellent generalization ability on different structures. Figures 7-9 show the performance superiority of our proposed method more clearly. In particular, it can be clearly seen that the performance fluctuation of our proposed method on multiple tasks is very small, which reflects the good adaptability of our proposed method to different task types. At the same time, to present the experimental results of our proposed method more vividly, we show the results of running on the JNU dataset in the form of t-SNE plots in Figure 10. We can see that our method performs well in all 10 tasks. Further analysis shows that the faults labeled 2 and 3 are easily confused, and problems of identification errors or unclear boundaries of these two types of faults occur in multiple tasks, indicating that there is still room for improvement in the recognition of these two faults. \begin{table}
\begin{tabular}{c c c} \hline
**Task** & **DANN** & **Proposed** \\ \hline V01 & 97.7 & 98.3 \\ V02 & 72.2 & 96.4 \\ V03 & 75.0 & 99.0 \\ V04 & 71.5 & 98.5 \\ V05 & 96.8 & 97.7 \\ V06 & 70.5 & 98.1 \\ V07 & 74.6 & 98.3 \\ V08 & 71.4 & 96.8 \\ \hline Avg & 78.7 & 97.9 \\ \hline \end{tabular}
\end{table} 
Table 6: The classification accuracy (%) of the VMS dataset.