## 2 Materials and Methods
### Methods
#### 2.2.1 Overview
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Evaluated** & \multicolumn{2}{c}{**4 s Extraction**} & \multicolumn{2}{c}{**10 s Extraction**} \\ \cline{3-5}  & **Metrics** & **14 Class** & **23 Class** & **14 Class** & **23 Class** \\ \hline \multirow{2}{*}{Vit} & Accuracy & 0.929 & 0.893 & 0.947 & 0.922 \\  & F1 score & 0.932 & 0.897 & 0.962 & 0.912 \\ \hline \multirow{2}{*}{DWFE + Vit} & Accuracy & 0.937 & 0.905 & 0.964 & 0.933 \\  & F1 score & 0.948 & 0.906 & 0.967 & 0.943 \\ \hline RoPE-DWTrans & Accuracy & 0.953 & 0.923 & 0.964 & 0.950 \\ (Ours) & F1 score & 0.961 & 0.928 & 0.967 & 0.951 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of four metrics using different classifiers on the 23-class dataset (4 s extraction). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Evaluated** & \multicolumn{2}{c}{**4 s Extraction**} & \multicolumn{2}{c}{**10 s Extraction**} \\ \cline{3-5}  & **Metrics** & **14 Class** & **23 Class** & **14 Class** & **23 Class** \\ \hline \multirow{2}{*}{Vit} & Accuracy & 0.929 & 0.893 & 0.947 & 0.922 \\  & F1 score & 0.932 & 0.897 & 0.962 & 0.912 \\ \hline \multirow{2}{*}{DWFE + Vit} & Accuracy & 0.937 & 0.905 & 0.964 & 0.933 \\  & F1 score & 0.948 & 0.906 & 0.967 & 0.943 \\ \hline RoPE-DWTrans & Accuracy & 0.953 & 0.923 & 0.964 & 0.950 \\ (Ours) & F1 score & 0.961 & 0.928 & 0.967 & 0.951 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment of RoPE-DWTrans. Figure 9: Fault categories and grouping: (a) 14-class classification experiments and (b) 23-class classification experiments. Additionally, we recorded the training fitting time for the gearbox fault diagnosis task. After introducing 2D RoPE, the model's training process showed significant acceleration. As shown in Figure 11, with the inclusion of 2D RoPE, the model converged more quickly, reducing the time and number of iterations required for training. Accuracy increased rapidly in the early stages of training. This indicates that 2D RoPE played a crucial role in enhancing the model's training efficiency and optimization process, significantly accelerating the model's adaptation to the training data and achieving better performance in a shorter amount of time. Figure 11: Accuracy improvement with different models in gearbox fault diagnosis. (**a**) Accuracy over epochs for 23-class 4 s extraction. (**b**) Accuracy over epochs for 23-class 10 s extraction. (**c**) Accuracy over epochs for 14-class 4 s extraction of Dataset B. (**d**) Accuracy over epochs for 14-class 10 s extraction. Figure 10: Performance comparison chart on precision and recall. (**a**) Evaluated metrics (4 s extraction); (**b**) evaluated metrics (10 s extraction). #### 3.3.2 Feature Extraction Performance Evaluation Under Multiple Time Windows

To systematically evaluate the performance of different filters in feature extraction, this paper conducted comparative experiments with 4 s and 10 s data extraction windows, exploring the impact of different filters on feature extraction effectiveness. The experimental results are detailed in Table 6.

#### 3.3.3 Classification of Gearbox Fault Types and Severity Levels

This paper further explored the accuracy of classifying the severity of each gearbox fault in the 23-class classification task. Accurately classifying gearbox fault types and their severity is crucial for improving fault diagnosis accuracy. The fault types are shown in Table 7. By accurately identifying the nature and development stage of the faults, engineers are able to take targeted maintenance actions in a timely manner, effectively avoiding the risks of over-maintenance or maintenance delays, optimizing equipment maintenance strategies, and reducing unnecessary costs. The experimental results are shown in Table 8 and Figures 12-14. Compared to the ViT, the proposed model demonstrated significant performance advantages in the industrial gearbox fault diagnosis task.