## 5 Conclusions  
In this paper, a migration learning model combining a domain-adversarial network and CRU is proposed for high-speed train bearing fault diagnosis under variable load conditions. The model has three advantages. Firstly, normalization is added to the feature extraction module to reduce the influence of individual difference samples on the network model, and the RELU activation function is used to improve the speed of the model opera  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **A-B** & **A-C** & **B-A** & **B-C** & **C-A** & **C-B** & **Average Accuracy** \\ \hline TCA & 76.9\% & 75.1\% & 74.2\% & 77.5\% & 74.4\% & 78.3\% & 76.1\% \\ JDA & 75\% & 74.4\% & 76.3\% & 76.5\% & 73.3\% & 76.7\% & 75.4\% \\ DACNN & 95.8\% & 96.7\% & 94.5\% & 97.5\% & 94.5\% & 97.3\% & 96.1\% \\ Proposed method & 99.1\% & 99\% & 99.3\% & 100\% & 99.1\% & 100\% & 99.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Test results of LY-GZ-02 test bench data.  
Figure 9: Confusion matrix for each experimental method.  
tion. Secondly the influence of each module in the feature extractor on the performance of the algorithm was analyzed through experiments, and it was finally determined that combining the feature extraction module with the CRU strengthens the model's ability to learn domain-invariant features. Finally, by experimentally analyzing the impact of the quantity of fully connected layers on the performance of the algorithm, it was determined that the ideal structure of the fault identification module and the domain discrimination module consists of two fully connected layers combined with the dropout function, which reduces the model parameters and the risk of model overfitting. In addition to this, the present method was compared with existing intelligent diagnosis methods to verify its good performance. However, this method only investigates fault diagnoses under different load conditions using the same equipment. Therefore, the next step is to study the migration fault diagnosis method for different types of bearings in different machines and the same equipment in high-speed trains, so as to better monitor the health status of high-speed train bearings.  
Conceptualization, Y.Z. and W.Z.; methodology, W.Z.; software, W.Z.; validation, W.Z., T.L. and X.Z.; data curation, W.Z., T.L. and X.Z; writing--original draft preparation, W.Z.; writing--review and editing, Y.Z. and Y.S.; visualization, W.Z.; supervision, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jilin Provincial Department of Science and Technology, grant number 20230101208 JC.  
Not applicable.  
The dataset used in Experiment 1 of this paper can be found at the following link: [[https://engineering.case.edu/bearingdatacenter/welcome](https://engineering.case.edu/bearingdatacenter/welcome)]([https://engineering.case.edu/bearingdatacenter/welcome](https://engineering.case.edu/bearingdatacenter/welcome)), accessed on 16 September 2024.  
The data from Experiment 2 in this study are available upon request from the corresponding author. For privacy reasons, these data are not publicly available.  
The authors declare no conflicts of interest.