## 2 Materials and Methods
### Methods
#### 2.2.1 Overview
The F1-score is a composite metric that combines the performance of precision and recall. \[\text{Accuracy}=\frac{TP+TN}{TP+FP+FN+TN} \tag{33}\]

\[\text{Precision}=\frac{TP}{TP+FP} \tag{34}\]

\[\text{Recall}=\frac{TP}{TP+FN} \tag{35}\]

\[\text{F1 score}=\frac{2\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{36}\]

### Performance

#### 3.3.1 Classification Performance

This paper designed experiments based on the MCC and HUST gearbox datasets to analyze 14 class and 23 class classifications. In the 14-class experiment, the HUST gearbox dataset was divided into three categories: normal gears, broken gears, and missing gears. The MCC dataset included one healthy state and six fault types: gear tooth missing, gear wear, tooth cracking, tooth fracture, tooth fracture with an inner bearing ring fault, and tooth fracture with an outer bearing ring fault. To further explore the mechanisms of bearing faults under different operating conditions and their signal characteristics, as well as the impact of fault severity on signal performance, the gear pitting category was used as an example. It was subdivided into four subcategories based on varying speed and torque conditions and fault severity levels, resulting in a total of 11 fault categories. These 11 MCC fault categories comprehensively covered all characteristics of the dataset, including different fault types, fault causes, and three fault severity levels: light, moderate, and severe. In the 23-class experiment, the classification method of the HUST gearbox dataset remained consistent with that of the 14-class experiment, dividing it into normal gears, broken gears, and missing gears. The MCC dataset included one healthy state and one missing gear state, with six typical fault types further subdivided into 20 categories based on light, moderate, and severe fault severity levels. This refined classification approach provided a more comprehensive representation of the fault characteristics and severity distribution in the dataset, offering critical insights for studying gear fault features and developing diagnostic methods. The method is shown in Figure 9.

In this paper, a combined dataset comprising the MCC dataset and the HUST gearbox dataset was used as experimental data. These two datasets were independent of each other, with no overlap or interaction between them. The dataset was divided into a training set and a test set at a ratio of 8.8:1.2, ensuring no duplicate samples existed between the two sets to guarantee the fairness and reliability of model evaluation. The detailed configuration of the key hyperparameters for the experiments is presented in Table 2. The experimental results are shown in Tables 3 and 4. This paper conducted a comparative experiment between the proposed method and the baseline ViT model. As shown in the tables and figures, the proposed method outperformed the baseline ViT model in four key metrics, indicating a significant performance improvement. The results are shown in Table 5 and Figure 10. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Evaluated Metrics**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline GRU + ShuffleNet & 0.927 & 0.932 & 0.925 & 0.919 \\ SeNetTrans & 0.938 & 0.944 & 0.954 & 0.943 \\ InceptionV3 & 0.859 & 0.841 & 0.858 & 0.843 \\ ConvNeXtV2 & 0.940 & 0.953 & 0.959 & 0.946 \\ RoPE-DWTrans (Ours) & 0.953 & 0.959 & 0.973 & 0.961 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of four metrics using different classifiers on the 14-class dataset (4 s extraction).