## 4 Case Study and Verification
### Experimental Parameter Configuration  
For the proposed intelligent fault diagnosis framework, the optimal hyperparameters, the batch size, the learning rate, and the dropout rate greatly affect the training speed, the effectiveness of extracted features, the classification accuracy, and the model robustness [20]. To select the optimal hyperparameters, the impact on the classification accuracy and time consumption of this model under different minimum numbers of batches, the initial learning rate and the dropout rate are shown in Figure 10, and the optimal model parameter configuration was determined. The adaptive moment estimation method is used to optimize the cross-entropy loss function and train the neural network framework. The average recognition accuracy and training time are taken as evaluation indicators, and the results are shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Healthy** \\ **Condition** \\ \end{tabular} & **Fault Diameter/mm** & **Training Samples** & **Testing Samples** & **Label** \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} A/B/C \\ 1200 r/min/1500 r/min \\ \end{tabular} } & Normal state & 0 & 300/300/300 & 100/100/100 & 1 \\ \cline{2-6}  & & 0.3 & 300/300/300 & 100/100/100 & 2 \\  & ball faults & 0.6 & 300/300/300 & 100/100/100 & 3 \\  & ball faults & 1.0 & 300/300/300 & 100/100/100 & 4 \\  & & 1.5 & 300/300/300 & 100/100/100 & 5 \\ \cline{2-6}  & & 0.3 & 300/300/300 & 100/100/100 & 6 \\  & Inner race fault & 0.6 & 300/300/300 & 100/100/100 & 7 \\  & Inner race fault & 1.0 & 300/300/300 & 100/100/100 & 8 \\  & & 1.5 & 300/300/300 & 100/100/100 & 9 \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Outer race fault \\ \end{tabular} } & 0.3 & 300/300/300 & 100/100/100 & 10 \\  & 0.6 & 300/300/300 & 100/100/100 & 11 \\ \cline{1-1}  & Outer race fault & 1.0 & 300/300/300 & 100/100/100 & 12 \\ \cline{1-1}  & & 1.5 & 300/300/300 & 100/100/100 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Organization of experimental dataset. Figure 9: Time-frequency representation and their histogram equalization for the roller table bearing under different health conditions. Figure 10 shows that the batch size has the greatest impact on the accuracy of this model, followed by the learning rate, and the maximum dropout rate has the smallest effect. In Figure 9(a), the accuracy and training times decrease as the minimum batch size increases. When the minimum number of batches increases from 16 to 32, the accuracy remains unchanged, although the training time decreases significantly. Therefore, 16 is selected as the optimal batch size. In Figure 9(b), the accuracy first increases and then decreases due to the increase in the learning rate, and the training time shows no significant change. The optimal effect is achieved when the learning rate is 0.01. In Figure 9(c), the accuracy is decreased when the dropout rate is increased, but the training time has no significant effect on the result. The optimal dropout rate is 0.3. Hence, the proposed neural network was trained with the optimal hyperparameters, and the typical training curve of the loss function and the training accuracy are shown in Figure 11, with stable fluctuations and network convergence. The fault diagnosis' average accuracy for the roller table bearing reaches 99.90%, and the training error is 0.13%. The confusion matrices of the training and validation sets are shown in Figure 12. The horizontal axis represents the real label, and the vertical axis represents the predicted label. Few samples were misjudged in each of the rolling element and inner circle fault states, and the rest were accurately identified, verifying the recognition accuracy of the proposed variable-scale attention mechanism-guided time-frequency feature fusion network. In order to verify the anti-noise interference ability and robustness of this proposed method, Gaussian white noise with different decibels is added into the experimental signal.