# Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning
## 3 Methods
### Overview of the Proposed Model's Architecture
Overall, this study not only makes breakthrough progress in the recognition of new fault types but also offers a new research direction and practical solution to address the challenges posed by limited source task data in identifying new fault types in target tasks, presenting broad prospects for practical application. While this study has achieved success on specific datasets, it has been conducted with a focus on different working conditions and tasks within the same dataset, without exploring the potential for cross-domain applications. Therefore, future work will investigate how to adjust or optimize the MBDCNet network and transfer learning strategies to cater to the needs of various industrial sectors, thereby broadening its application scope in industrial equipment maintenance. Moreover, overfitting problems may occur during transfer learning, especially when there are relatively few data in the target domain. We will continue to explore how to overcome these limitations through various strategies in future work. Conceptualization, F.Z. and Z.Y. ; methodology, F.Z. and Z.Y. ; software, F.Z. ; validation, F.Z., Z.Y. and G.X. ; investigation, F.Z. and G.H. ; resources, Z.Y. ; data curation, Z.Y. and Y.B. ; writing--original draft preparation, Z.Y. and F.Z. ; writing--review and editing, F.Z. and Z.Y. ; visualization, G.X. and G.H. ; supervision, Z.Y. and Y.B. ; project administration, Z.Y. ; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript. National Key R & D Program of China under Grant No. 2017 YFE0125300. Not applicable. The data presented in this study are available on request from the corresponding author. They are restricted to experimental results. Figure 10: t-SNE plots for each task on the JNU dataset using the proposed method. ## References

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