# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 4 Experiments
### Experimental Performance and Analysis  
We show the experimental results on three datasets in Tables 4-6. First, we analyze the experimental results on the COWRU dataset. On the whole, the accuracy of our proposed method is significantly higher than that of the existing methods, which shows that our method is superior to the existing methods in performance. Below we analyze the performance on specific tasks in detail. Existing methods perform relatively poorly on the three tasks C03, C20, and C30, which we first focus on analyzing. In terms of fault type  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Task** & \begin{tabular}{c} **Source\(\rightarrow\)Target** \\ **(Motor Load)** \\ **(hp)** \\ \end{tabular}  & \begin{tabular}{c} **Source\(\rightarrow\)Target** \\ **(Motor Speed)** \\ **(rpm)** \\ \end{tabular}  & \begin{tabular}{c} **Source Label Set** \\ **(rpm)** \\ \end{tabular}  & \begin{tabular}{c} **Target Label Set** \\ **(rpm)** \\ \end{tabular}  \\ \hline \multirow{9}{*}{CWRU} & C01 & 0\(\rightarrow\)1 & 1797\(\rightarrow\)1772 & 0, 1, 2, 3, 4, 5, 6 & 0, 1, 2, 3, 4, 7, 8, 9 \\  & C02 & 0\(\rightarrow\)2 & 1797\(\rightarrow\)1750 & 0, 1, 2, 3, 4, 5, 6 & 0, 1, 2, 3, 4, 7, 8, 9 \\  & C03 & 0\(\rightarrow\)3 & 1797\(\rightarrow\)1730 & 0, 1, 2, 3, 4, 5 & 0, 1, 2, 3, 4, 7, 8 \\  & C10 & 1\(\rightarrow\)0 & 1772\(\rightarrow\)1797 & 0, 1, 2, 3, 4, 5 & 0, 1, 2, 3, 4, 7, 8 \\  & C12 & 1\(\rightarrow\)2 & 1772\(\rightarrow\)1750 & 0, 1, 2, 3, 4, 5 & 0, 1, 6, 7 \\  & C13 & 1\(\rightarrow\)3 & 1772\(\rightarrow\)1730 & 0, 1, 2, 3, 4, 5 & 0, 1, 6, 7 \\  & C20 & 2\(\rightarrow\)0 & 1750\(\rightarrow\)1797 & 0, 1, 3, 5, 7, 9 & 0, 1, 2, 3, 4, 5 \\  & C21 & 2\(\rightarrow\)1 & 1750\(\rightarrow\)1772 & 0, 1, 3, 5, 7, 9 & 0, 1, 2, 3, 4, 5 \\  & C23 & 2\(\rightarrow\)3 & 1750\(\rightarrow\)1730 & 0, 1, 3, 5, 7 & 0, 1, 2, 3, 4 \\  & C30 & 3\(\rightarrow\)0 & 1730\(\rightarrow\)1797 & 0, 1, 3, 5, 7 & 0, 1, 2, 3, 4 \\  & C31 & 3\(\rightarrow\)1 & 1730\(\rightarrow\)1772 & 0, 2, 4, 6, 8 & 0, 2, 5 \\  & C32 & 3\(\rightarrow\)2 & 1730\(\rightarrow\)1730 & 0, 2, 4, 6, 8 & 0, 2, 5 \\ \hline \multirow{9}{*}{JNU} & J01 & \multirow{9}{*}{\ \begin{tabular}{c} J02 \\ J03 \\ J04 \\ J05 \\ J06 \\ J07 \\ J08 \\ J09 \\ J10 \\ \end{tabular}  } &
\begin{tabular}{c} 0, 1, 2, \\ 0, 1, 3 \\ 0, 1 \\ 0, 2 \\ 0, 2 \\ 0, 1, 2, 3 \\ 00, 1 \\ 0, 2 \\ 0, 2 \\ 0, 3 \\ 0, 1 \\ 0, 2 \\ 0, 3 \\ 0, 3 \\ 0, 1, 2, 3 \\ 0, 1,distribution, the main characteristics of these three tasks are as follows: (1) C03 is mainly characterized by containing different fault categories; (2) C20 and C30 contain different fault sizes of the same fault category. As we have introduced in the previous chapter, for the open-domain adaptation problem, the unknown type faults existing in the target domain should be classified into the same additional type. Therefore, we can analyze that in the C03 task, the existing methods fail to accurately identify the two unknown fault categories of label 7 and 8. And it is confused with the existing fault type. In the two tasks C20 and C30, the existing method incorrectly identifies the unknown fault size, which is confused with the known type. However, our proposed method achieves significant improvement in these three tasks. Therefore, our proposed method can accurately extract the characteristics of faults, especially the characteristics of different fault sizes, which is of great significance for judging the danger degree of faults. Further, we compare these three tasks with the corresponding tasks C10, C21, and C23 of the same fault type, and it can be seen that the accuracy still lags behind. The main difference in comparison is the load situation. In the case of a large load difference, the accuracy will be significantly reduced, and the accuracy of our proposed method will also be reduced. However, the reduction is very little, which confirms that our proposed method has better generalization ability for load changes in different working conditions.