# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 5 Conclusions  
This paper proposes an unknown bearing fault diagnosis method based on transfer learning, which realizes the diagnosis of unknown faults by defining the problem as an open set domain adaptation problem. We introduce the multi-scale convolution module and AcConv module to design the MCRCNet feature extraction network, which realizes the extraction of key features of bearing faults at multiple scales and the elimination of redundant features, so as to better meet the high requirements of transfer learning for feature extraction. At the same time, in order to improve the performance of the method, we introduce an improved loss function, which not only improves the stability of the model under the condition of data imbalance, but also improves the efficiency of adversarial training, which significantly improves the robustness and generalization ability of the model. We validate our proposed method on the CWRU dataset, the JNU dataset, and the VMS dataset from our laboratory. Experimental results show that our proposed method achieves high accuracy in a variety of task conditions and is advanced compared with existing methods. In conclusion, this paper proposes a new and feasible research method for the unknown bearing fault field, which has a good application prospect.  
Although the method proposed in this paper has been advanced, the problem we study concerning the fault diagnosis of a unified dataset or the same device remains, and we have not been able to extend the method to the field of unknown fault diagnosis across devices. Therefore, in future work, we will further optimize the transfer learning method and our proposed feature extraction network to achieve wider application in the field of bearing fault diagnosis. In addition, in the field of fault diagnosis, there is an extreme case where only normal bearing data can be obtained. In future work, we will also try to study how to achieve fault diagnosis in this extreme case.  
Conceptualization, G.X. and F.Z.; methodology, G.X. and R.G.; software, G.X. and F.Z.; validation, G.X.; formal analysis, G.X. and Z.Y.; investigation, G.X. and Z.Y.; resources, Z.Y. and R.G.; data curation, F.Z.; writing--original draft preparation, G.X.; writing--review and editing, G.X., R.G. and Z.Y.; visualization, G.X.; supervision, Z.Y. and F.Z.; project administration, Z.Y.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Major Project of Liaoning Province \"Key Technologies and Application Development of Intelligent Design\".  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures](https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures)]([https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures](https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures)), [[http://www.52](http://www.52) phm.cn/datasets/bear/Bearing-data-set-of-Jiangnan-University.html]([http://www.52](http://www.52) phm.cn/datasets/bear/Bearing-data-set-of-Jiangnan-University.html) (accessed on 1 January 2025).  
The authors declare no conflicts of interest.  
Figure 10: t-SNE plots for each task on the JNU dataset using the proposed method.