## 2 Materials and Methods
### Methods
#### 2.2.1 Overview
Within the 4 s extraction feature extraction window, the proposed model improved the classification accuracy for Fault 1 and Fault 2 by 3.7% and 3.3%, respectively, and increased the accuracy for Fault 4 and Fault 6 by 3.5% and 5.4%, respectively. For the 10 s extraction window, the proposed model achieved the same classification accuracy as the ViT method for Fault 1, both reaching 100%, but outperformed it by improving the accuracy for Fault 2 and Fault 6 by 4.2% and 5.0%, respectively. These results indicate that the proposed method was effective in distinguishing between different fault types and their severity, providing strong technical support for equipment fault diagnosis in practical applications. This research not only improved fault diagnosis accuracy but also provided a theoretical basis for the construction and optimization of intelligent maintenance systems. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Future Extraction**} & \multirow{2}{*}{**Datasets**} & \multicolumn{4}{c}{**Evaluated Metrics**} & \multirow{2}{*}{**Training**} \\ \cline{3-3} \cline{5-6}  & & **Accuracy** & & & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{2}{*}{4 s extraction} & 14 class & 0.953 & 0.959 & 0.973 & 0.961 & 51s \\  & 23 class & 0.923 & 0.932 & 0.928 & 0.928 & 122 s \\ \hline \multirow{2}{*}{10 s extraction} & 14 class & 0.964 & 0.976 & 0.971 & 0.967 & 33 s \\  & 23 class & 0.950 & 0.949 & 0.966 & 0.951 & 95 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of different feature extraction method performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extraction**} & **Fault** & **Fault 1** & **Fault 2** & **Fault 3** & **Fault 4** & **Fault 5** & **Fault 6** \\ \cline{2-6}  & **Class** & **0–2** & **3–5** & **8–10** & **11–13** & **14–16** & **17–19** \\ \hline \multirow{2}{*}{4 s extraction} & ViT & 0.833 & 0.854 & 0.897 & 0.923 & 0.873 & 0.915 \\  & Ours & 0.870 & 0.887 & 0.870 & 0.958 & 0.943 & 0.969 \\ \hline \multirow{2}{*}{10 s extraction} & ViT & 1.000 & 0.958 & 0.933 & 0.933 & 0.857 & 0.833 \\  & Ours & 1.000 & 1.000 & 1.000 & 0.933 & 0.928 & 0.883 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Gearbox fault types. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extraction**} & **Fault** & **Fault 1** & **Fault 2** & **Fault 3** & **Fault 4** & **Fault 5** & **Fault 6** \\ \cline{2-6}  & **Class** & **0–2** & **3–5** & **8–10** & **11–13** & **14–16** & **17–19** \\ \hline \multirow{2}{*}{4 s extraction} & ViT & 0.833 & 0.854 & 0.897 & 0.923 & 0.873 & 0.915 \\  & Ours & 0.870 & 0.887 & 0.870 & 0.958 & 0.943 & 0.969 \\ \hline \multirow{2}{*}{10 s extraction} & ViT & 1.000 & 0.958 & 0.933 & 0.933 & 0.857 & 0.833 \\  & Ours & 1.000 & 1.000 & 1.000 & 0.933 & 0.928 & 0.883 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification results of gearbox fault types and severity levels (accuracy). #### 3.3.4 Comparison of Different Modules

To further validate the generality and effectiveness of the RoPE-DWTrans method, we conducted comparative experiments with multiple mainstream deep learning models. During the model training process, the RoPE-DWTrans hyperparameter settings listed in Table 2 were strictly followed. The experiments evaluated the classification of one healthy class and five fault classes from the MCC5 gearbox dataset, and the detailed classification results of each model are presented in Table 9. The experimental results demonstrated that the RoPE-DWTrans exhibited superior classification capability in multi-class fault diagnosis tasks. Figure 12: Evaluated metrics Improvement with different models in gearbox fault diagnosis (4 s extraction). Figure 13: Evaluated metrics’ improvement with different models in gearbox fault diagnosis (10 s extraction). ## 4 Discussion

To effectively achieve bearing fault diagnosis, this paper proposed an innovative method based on the ViT model.