## 3 Experiments
### Transfer Results  
#### 3.2.1 Comparison Methods  
To verify the effectiveness of RASAN-W, the transfer results of RASAN-W and current bearing fault diagnosis methods were compared. ResNet only uses the feature extractor and label classifier in Figure 3 as the overall network structure. This method only uses source domain samples for training and does not consider target samples. After training is completed, it is tested on target domain samples and does not involve domain adaptation. Deep domain confusion (DDC) is a metric-based domain adaptation method [8]. MMD is used to reduce the discrepancy in first-order statistics and align the domain global distribution. DANN consists of three parts: feature extractor, label classifier, and domain discriminator [13]. During the DANN training process, the feature extractor and label classifier are trained simultaneously to minimize the classification error, and the domain discriminator is trained to maximize the loss of the domain classifier, thus forcing the feature extractor to learn features that are insensitive to domain information. Compared directly with the DANN method, DSAN focuses on solving the discrepancies between subdomains [12]. DSAN introduces LMMD as one of the losses of the label classifier. This measure enables DSAN to focus on interdomain class information while aligning the global distribution. The key idea of DASAN is to introduce the idea of adversarial training into DSAN [14]. At the same time, DASAN introduces pseudo-label loss in the label classifier to further improve the accuracy of fault diagnosis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Load** & **Fault Type** & **Number of Train Sample** & **Number of Text Sample** \\ \hline \(C_{0}\) & 0 HP & Normal, IF, OF, RF & 5600 & 400 \\ \(C_{1}\) & 1 HP & Normal, IF, OF, RF & 5600 & 400 \\ \(C_{2}\) & 2 HP & Normal, IF, OF, RF & 5600 & 400 \\ \(C_{3}\) & 3 HP & Normal, IF, OF, RF & 5600 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the bearing fault dataset at Case Western Reserve University. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **RPM** & **Fault Type** & **Number of Train Sample** & **Number of Text Sample** \\ \hline \(J_{0}\) & 600 & Normal, IF, OF, RF & 1600 & 400 \\ \(J_{1}\) & 800 & Normal, IF, OF, RF & 1600 & 400 \\ \(J_{2}\) & 1000 & Normal, IF, OF, RF & 1600 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the bearing fault dataset at Jiangnan University. In the training phase of the RASAN-W model, the following parameter configuration is employed: To ensure that the label classifier effectively captures fault features and suppress the domain noise during the initial training phase, the trade-off parameters will be incrementally modified \(\alpha\):  
\[\alpha=\frac{\exp(l\cdot s)-1}{\exp(l\cdot s)+1} \tag{13}\]  
where \(\alpha\) is set to 1 and \(p\) is the ratio of the current training batch to the maximum training batch. \(\alpha\) is set to 0.1, \(\mu\) is selected from [0.001, 0.01, 0.1], and \(\delta\) is set to 0.1. SGD is selected as the optimizer to update the parameters of the network, the momentum is 0.9, and the weight decay is \(5*10^{-4}\). In the process of introducing the Wasserstein metric to measure the distribution discrepancy between the source domain and the target domain, to avoid the gradient explosion problem that may occur during the model training process, this article uses the L2 norm to clip the gradient. When the L2 norm of the gradient is greater than the threshold, the gradient clipping method is used to clip excessively large gradient values to a specified range. The gradient clipping function is formulated as follows:  
\[\
abla_{w}L_{total}= \begin{cases}\
abla_{w}L_{total}&\text{if}(\|\
abla_{w}L_ {total}\|\leq G_{norm})\\ \
abla_{w}L_{total}\times clip_{coef}&\text{if}(\|\
abla_{w}L_{total}\|\geq G _{norm})\end{cases}