# FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization
## 4 Experiment
### Evaluation in CWRU Bearing Dataset
Figure 9 shows several ways of using multiple source domains in the generalization of domain 3 results. As can be seen from the table, the multi-source domain model generalizes significantly better than the dual-source domain model. In the experiment of dual-source domain, FSN performs poorly because the feature shift network is only trained on source domain \(2\to 1\), and the lack of domain makes the feature shift network unable to learn a good feature shift. In the multi-source domain experiment, FSN achieves the best generalization performance. The FSN effectively utilizes the information carried by the domain labels of the target domain and identifies the relationships between features in adjacent domains. Figure 9 simultaneously shows the effect of each model using the FC classifier's generalization and the relationship between the generalization effect of the classifier. We can see that the multi-source method is significantly better than the dual-source method, and FSN has the largest performance improvement when moving from dual-source to multi-source domains. In addition, we compared FSN with several of the latest domain generalization techniques. Table 2 demonstrates that FSN outperforms these methods, largely due to its effective utilization of the LD information. As detailed in the training setup, we selected domain 3 as the target domain. This choice is crucial in Algorithm 3, as it promotes the alignment of features from high-level domains with those from low-level domains sharing the same label during training. Figure 9: Model generalization effect comparison. **Visual Analysis. ** Figure 10 shows that FSN is still 100% and can be obtained in most of the failure modes of classification performance. For the B021 and IR014 faults (for more information, refer to Table A1), the error of the FSN is substantial, as evidenced by only 48.64% and 54.05% being correctly classified, respectively. Among them, 27.02% of IR014 faults are identified as B021 faults, and 18.91% are identified as IR021 faults. For the B021 fault, 40.54% of the data are identified as IR014 faults. Figure 11 illustrates the use of the t-SNE method for visualizing data distributions. The failure modes are denoted by their corresponding numbers, as listed in Table A1. When the test set data are visualized in a grayscale image format, many data points of the same class cluster together, exhibiting initial signs of locality in the feature space. For example, data points from the same class tend to form coherent groups. Specifically, data associated with faults 1 and 8 show a central and cohesive distribution pattern. However, other fault types exhibit more dispersed distributions. For example, data points for fault 3 are scattered across the upper region of the plot, while fault 2 forms a semi-circular distribution around fault 1. Additionally, faults 7 and 0 display a near-uniform mixed distribution. Notably, in subsequent experiments, faults 1 and 8 achieve 100% classification accuracy when the model is applied to the test set. This high accuracy not only indicates that faults 1 and 8 are particularly distinguishable, but it also underscores the effectiveness of the t-SNE plot in reflecting the underlying data distribution patterns. In addition, Figure 11 also shows that the clusters of fault 4 and 8 are close to each other, indicating that fault classes 4 and 8 may be difficult to separate in the classification. This is confirmed in the confusion matrix, where 20% of fault 4 is classified as fault 8. Nearly 25% of the classified results for fault 8 are incorrectly classified as fault 4, which is the only way to confuse fault 8. The feature output by the feature extractor of FSN is represented in Figure 12a, and the feature output of the features in the figure after the feature shift network is shown in Figure 12b. As shown, after the shift, the distribution of the features from different domains becomes more aligned. Notably, the intensity or concentration of these features before and after the shift appears to be roughly equivalent.