# FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization
## 5 Conclusions and Future Work  
This paper proposes LD domain generalization as a specific example of domain generalization, fully uses the particularity of the bearing dataset used for fault diagnosis, and implements FSN in this scenario. The domains in LD domain generalization correspond to the actual physical meaning (horsepower). FSN learns the relationship between these domains and, finally, shifts the data features in the target domain into the source domain for classification. Experimental results demonstrate that MCDANN (mixed classifier into DANN) surpasses the current DANN with higher generalization accuracy on the CWRU dataset, achieving accuracy rates of 82.6% and 81.0%, respectively. Experiments also reveal that FSN outperformed the comparison model's generalization performance by 1% to 2% on the CWRU dataset and reached 85.22% accuracy on the rotated MNIST dataset.  
Nevertheless, the application of FSN exhibits certain limitations. Addressing these constraints is a key aspect of future research efforts.  
* Firstly, FSN is only tested on the CWRU bearing dataset and the rotated MNIST dataset. The model needs to be tested on additional datasets with similar characteristics to validate its efficiency.
* Secondly, the proposed FSN requires at least three source domains, and the domain number is required to correspond to the actual physical information. In order to make other datasets meet this requirement, the datasets need to be selected carefully, and the appropriate domain partition method should be selected.
* Lastly, under the premise that the domain number corresponds to actual physical information, the source domain numbering needs to be consecutive during the training process and can only be generalized relatively well from the target domain to the adjacent source domains. Better methods for discontinuous domain generalization need to be explored.  
Therefore, the application of FSN models remains limited. Addressing and mitigating these constraints is a key objective for future research.  
Funding acquisition, H.C.; methodology, H.C.; software, E.Z., Y.J., and L.S.; validation, E.Z., Y.J., and L.S.; formal analysis, H.C., L.S., and E.Z.; investigation, E.Z., Y.J., and L.S.; resources, H.C.; data curation, E.Z. and Y.J.; writing--original draft, E.Z. and Y.J.; supervision, H.C.; project administration, H.C. and L.S.; writing--review and editing, E.Z. and Y.J. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Models** & **KDA** & **UB** & **DICA** & **SCA** & **MATE** & **ERM** & **DANN** & **DeepC** & **DeepN** & **CIDDG** & **DIFEX** & **VREx** & **GroupDRO** & **ANDMask** & **FSN** \\ \hline Acc & 72.81 & 69.39 & 72.05 & 73.43 & 78.34 & 79.56 & 82.95 & 80.08 & 83.99 & 84 & 76.28 & 82.96 & 83.75 & 82.83 & **85.22 \({}^{*}\)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Generalization accuracy of each model on the rotated MNIST dataset.  
**Funding:** This research was funded by the China National Key R&D Program during the 13 th Five-Year Plan Period (Grant No. 2018 YFB1700405).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)]([https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)) (accessed on 12 June 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.