## 4 Experimental Validation
### Case 1
#### 4.1.3 Image Comparison Experiments  
To verify the feasibility of the MCR image processing method, this paper conducts comparative experiments using MTF, CWT, and RP images. To ensure the rigor of the experiments, KAResNe is selected as the backbone model, and algorithms integrating MK-MMD and CDAN+E are employed as the domain adaptation methods across all comparisons.  
Table 6 shows that the MCR-KAResNet-TLDAF method achieved the highest accuracy. Compared to the RP-KAResNet-TLDA method, it improved by 21.54%, and compared to the MTF-KAResNet-TLDA method, it improved by 43.64%. Additionally, it outperformed the CWT-KAResNet-TLDA method by 1.916%. The MCR-KAResNet-TLDAF method surpassed the RP-KAResNet-TLDA and MTF-KAResNet-TLDA methods across all transfer tasks. Although it showed a decline in accuracy for the 0-2 and 2-0 transfer tasks compared to the CWT-KAResNet-TLDA method, it achieved improvements of 14.8% and 10.67% in the 3-0 and 3-1 transfer tasks, respectively. In the 3-0 and 3-1 transfer tasks, it broke through the bottleneck where the accuracy was below 90% for the other three conditions.  
Compared to the other three image processing methods, MCR provides a richer depiction of signal features, offering a more comprehensive feature base for the subsequent model to recognize and diagnose faults. This comprehensive approach ensures that the  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Methods** & **0-1** & **0-2** & **0-3** & **1-0** & **1-2** & **1-3** & **2-0** & **2-1** & **2-3** & **3-0** & **3-1** & **3-2** & **Average** \\ \hline MCR-KAResNet-TLDAF & 100 & 99.87 & 100 & 100 & 100 & 97.6 & 100 & 100 & 94.8 & 100 & 100 & **99.36** \\ RP-KAResNet-TLDAF & 66.8 & 66.67 & 67.33 & 81.81 & 84.27 & 81.73 & 81.14 & 79.47 & 85.33 & 78.48 & 77.87 & 82.93 & 77.82 \\ MTF-KAResNet-TLDAF & 52.13 & 58.8 & 51.47 & 61.33 & 60.27 & 53.47 & 55.33 & 61.2 & 55.73 & 48.67 & 52.53 & 57.73 & 55.72 \\ CWT-KAResNet-TLDAF & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 80 & 89.333 & 100 & 97.444 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of the image fusion method on the CWRU dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Group** & **Methods** & **0-1** & **0-2** & **0-3** & **1-0** & **1-2** & **1-3** & **2-0** & **2-1** & **2-3** & **3-0** & **3-1** & **3-2** & **Average** \\ \hline \multirow{3}{*}{First group} & 0.2 RP-MIT-0.2 CWT & 99.33 & 99.6 & 95.73 & 99.6 & 100 & 99.87 & 99.47 & 99.6 & 99.87 & 96 & 97.07 & 100 & 98.84 \\  & 0.2 CWT-RP-0.2 MTF & 84.53 & 87.33 & 81.73 & 98.4 & 98.93 & 99.6 & 99.07 & 98.93 & 99.73 & 94.53 & 95.73 & 99.73 & 94.86 \\  & **0.2 MTF-CWT-0.2 RP** & 100 & 99.87 & 100 & 100 & 100 & 100 & 97.6 & 100 & 100 & 94.8 & 100 & 100 & **99.36** \\ \hline \multirow{3}{*}{Second group} & 0.2 MTF-0.2 RP-CWT & 90 & 76.53 & 70 & 95.73 & 100 & 100 & 90.93 & 94.27 & 100 & 76.27 & 100 & 100 & 91.14 \\  & CWT-0.2 MTF-0.2 RP & 100 & 100 & 99.73 & 100 & 100 & 100 & 95.87 & 96.4 & 100 & 79.6 & 100 & 100 & 97.63 \\  & **0.2 MTF-CWT-0.2 RP** & 100 & 99.87 & 100 & 100 & 100 & 100 & 97.6 & 100 & 100 & 94.8 & 100 & 100 & **99.36** \\ \hline \multirow{3}{*}{Third group} & 0.1 MTF-CWT-0.1 RP & 100 & 99.87 & 99.87 & 100 & 100 & 100 & 97.33 & 99.73 & 100 & 95.07 & 93.33 & 100 & 98.77 \\  & MTF-CWT-RP & 100 & 99.73 & 100 & 100 & 100 & 100 & 93.87 & 94 & 100 & 93.87 & 100 & 100 & 98.46 \\ \cline{1-1}  & 0.2 RP-CWT-0.2 MTF & 100 & 100 & 100 & 100 & 100 & 93.6 & 100 & 100 & 89.2 & 100 & 100 & 98.57 \\ \cline{1-1}  & **0.2 MTF-CWT-0.2 RP** & 100 & 99.87 & 100 & 100 & 100 & 100 & 97.6 & 100 & 100 & 94.8 & 100 & 100 & **99.36** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of the image fusion method on the CWRU dataset.  
model can leverage a broader spectrum of diagnostic information, leading to improved fault identification performance.