# Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning
## 3 Methods
### Overview of the Proposed Model's Architecture
During this stage, the model employs cross-entropy loss and CORAL (domain-adaptive loss) for meticulous parameter adjustment, successfully learning the generic vibration features in the source domain, laying a solid foundation for subsequent transfer learning. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multicolumn{2}{c}{**MBDCNet**} & \multicolumn{2}{c}{**UDA**} & \multicolumn{2}{c}{**EVT-OSFD**} \\ \cline{2-7}  & **all\_acc** & **known\_acc** & **all\_acc** & **known\_acc** & **all\_acc** & **known\_acc** \\ \hline C01 & **99.8** & 99.8 & 79.9 & 93.3 & 83.8 & 94.4 \\ C02 & **100.0** & 100.0 & 78.1 & 89.1 & 81.5 & 93.7 \\ C03 & **85.2** & 80.9 & 70.8 & 80.8 & 78.6 & 85.7 \\ C10 & **97.6** & 96.4 & 80.7 & 89.4 & 84.1 & 90.2 \\ C12 & **97.2** & 96.3 & 84.6 & 96.5 & 90.7 & 98.9 \\ C13 & **96.3** & 95.1 & 79.0 & 93.3 & 84.4 & 94.2 \\ C20 & **96.2** & 96.2 & 70.5 & 76.3 & 82.9 & 82.3 \\ C21 & **92.3** & 96.2 & 78.4 & 83.1 & 83.9 & 87.0 \\ C23 & **91.2** & 91.2 & 78.4 & 80.0 & 87.3 & 89.8 \\ C30 & **89.2** & 89.2 & 68.6 & 73.4 & 74.4 & 73.7 \\ C31 & 83.8 & 83.8 & 71.3 & 75.9 & **94.9** & 94.3 \\ C32 & 85.2 & 85.2 & 74.7 & 79.9 & **96.3** & 96.2 \\ \hline Average & **92.8** & 92.2 & 77.3 & 86.3 & 85.2 & 85.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The classification accuracy (%) of CWRU dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multicolumn{2}{c}{**MBDCNet**} & \multicolumn{2}{c}{**OSDAFD**} & \multicolumn{2}{c}{**DAN-COSFD**} \\ \cline{2-7}  & **all\_acc** & **known\_acc** & **all\_acc** & **known\_acc** & **all\_acc** & **known\_acc** \\ \hline J01 & **96.03** & 98.25 & 75.22 & 75.22 & 79.52 & 79.52 \\ J02 & **96.54** & 98.12 & 64.92 & 65.40 & 72.30 & 70.01 \\ J03 & **95.26** & 98.80 & 67.58 & 63.02 & 82.00 & 76.68 \\ J04 & **94.53** & 97.75 & 70.39 & 70.04 & 55.35 & 72.46 \\ J05 & **96.03** & 98.70 & 74.55 & 78.17 & 93.02 & 95.13 \\ J06 & 83.05 & 85.01 & 56.87 & 60.36 & **85.32** & 83.24 \\ J07 & **88.04** & 91.62 & 57.85 & 64.23 & 87.59 & 91.55 \\ J08 & 77.41 & 77.23 & 64.63 & 64.92 & 75.46 & 67.14 \\ J09 & 87.87 & 91.54 & 68.43 & 83.68 & **90.73** & 97.08 \\ J10 & **86.89** & 90.76 & 67.95 & 73.81 & 85.71 & 90.17 \\ \hline Average & **90.17** & 92.78 & 66.84 & 69.89 & 80.70 & 82.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The classification accuracy (%) of JNU dataset. In the transfer learning phase, these pre-trained parameters are applied to the target domain, combined with an improved loss function and adjusted learning rate, further enhancing the model's performance. Fine-tuning allows our network to undergo adjustments based on the target data, better adapting to new environments. Despite the limited volume of data in the target domain, it is sufficient to support the effective fine-tuning of the network without the necessity of training from scratch. This approach enables us to retain the general features learned by the pre-trained model while also optimizing the model for specific tasks. Furthermore, fine-tuning helps to balance the information from the source and target domains during the transfer learning process, thereby preventing overfitting to the source domain data or underperforming in the target domain. This balance is crucial for the success of transfer learning, ensuring efficient knowledge transfer to the target domain and enhancing model performance and adaptability in new scenarios. Overall, the experimental results clearly demonstrate that our method, with its meticulously designed model structure, its efficient pre-training and transfer learning strategies, and a loss function that thoroughly considers both source and target domain information, exhibits exceptional generalization in bearing fault diagnosis tasks. This ensures the model's reliability and accuracy when dealing with complex real-world application scenarios.