## 3 Experimental Verification
### Discussion  
In this section, SRMANet is compared with other deep learning methods to demonstrate its superiority in learning representative features. To ensure the validity of the results, the validation experiments were compared using the code open-source benchmark model [43]. The models compared include one-dimensional convolutional neural networks (CNN 1d), one-dimensional residual neural networks (ResNet 1d), one-dimensional stacked self-encoders (SAE 1d), bi-directional long and short-term memory networks (Bi-LSTM), and the classic LeNet and AlexNet. The two-dimensional DNN model uses time-domain images as validation data. Figure 13 shows the recognition performance of different DNN models based on five-fold cross-validation. Table 4 shows the accuracy of all DNN methods in Fold-1, where  
Figure 12: SRMANet Middle Layer Visualization. (**a**) Input layer (**b**) Conv block1 output (**c**) Conv block2 output (**d**) Conv block3 output (**e**) Attention unit output (**f**) The last layer. SRMANet achieves an impressive 100% accuracy in the C6 and C11 recognition tasks. It is easy to see that SRMANet performs better in the analysis of complex data, such as those related to planetary gearboxes. Datasets with different sample lengths are selected separately for validation without changing the model structure and parameters. According to Table 5, the sensitivity of SRMANet to changes in sample lengths is found to be low, indicating that it is able to perform well in a number of generic fault diagnosis tasks. On the other hand, the accuracy of validation under the same dataset still lacks reliability, so tests of model generalization capabilities need to be carried out. The generalization ability of the model is still one of the problems that are difficult to overcome at the moment, and transfer learning provides us with ideas. However, it was found through validation that this type of transfer method only performs well with fewer working conditions and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **SRMANet** & **CNN 1d** & **ResNet 1d** & **SAE 1d** & **Bi-LSTM** & **Lenet 2d** & **Alexnet 2d** \\ \hline C0 & **99.98** & 97.53 & 94.76 & 98.71 & 79.93 & 81.37 & 90.42 \\ C1 & **99.70** & 98.41 & 96.13 & 99.93 & 84.71 & 84.72 & 90.7  
distinct data characteristics. Therefore, transfer learning should be enhanced from another perspective, i.e., by designing models that extract representative features  
Learning representative features requires models with stronger generalization capabilities and adaptability to secondary factors. Load and speed, as typical secondary factors, are very likely to interfere with the diagnosis results in fault diagnosis tasks, which is one of the reasons why transfer learning is currently a hot topic. Here we mainly consider the robustness of the model. Large differences were found in the distribution of the data for the same fault at different speeds and loads. We performed a load adaptation analysis (Table 6) and a speed adaptation analysis (Table 7) by means of a model-based transfer learning approach. The arrows indicate the transfer process of the model between different working conditions. It can be seen that the performance of SRMANet decreased significantly under changing loads and speeds, which is due to the change in data distribution. Figure 14 compares the results against those of other models, and shows that the adaptability of the proposed SRMANet model still maintains a slight advantage. This indicates that SRMANet as a feature extractor is more representative and general in the features it obtains.