## 3 Experiments and Results
### Ablation Experiments
#### 3.4.2 Different Model Combinations  
To verify the effectiveness of the proposed method, different module combinations are used to identify TFCF images. The average recognition accuracy and standard deviation of ten experiments are recorded in Table 4, and the tick mark under the module in Table 4 indicates that the module is adopted in the recognition method. The variation curves of training set loss and validation set accuracy of high accuracy models obtained by different methods in ten experiments are shown in Figure 7. It can be seen that the proposed model only converges after 7 epochs, which is faster than other methods.  
Overall, the proposed method has the best recognition effect and the fastest convergence speed.  
By comparing the experimental results of methods 4 and 5, it can be seen that the recognition effect is greatly improved after CNN is added to the neural network. Because the same time-frequency energy appearing in different positions of time-frequency images is essentially different, the traditional neural network directly reconstructs the tensor,  
Figure 5: Training of RTFANet model under different inputs: (**a**) curve of training set loss; (**b**) curve of the accuracy of the test set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Time-Frequency Image** & **Average Recognition Accuracy (\%)** & **Standard Deviation (\%)** \\ \hline TFCF & **99.80** & 0.02 \\ MSST & 94.13 & 0.09 \\ TMSST & 93.51 & 0.06 \\ STFT & 85.62 & 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RTFANet recognition results of different time frequency images input.  
completely ignoring the position information of the image. Since RCA introduces residual structure to reduce the information loss of eigenmatrix after channel weighting, it can be seen from methods 2 and 3 that RCA has a better effect than the channel attention mechanism in traditional SENet [48]. Comparing the experimental results of method 1 and method 2, it can be seen that the effect of using only the channel attention mechanism is not as good as adding an attention mechanism in all three dimensions. The main signal components of each fault type are different, and the time and frequency dimensions correspond to SCRVF and SCSVF, respectively. Time and frequency need to be further assigned to the weight of the network.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **CNN** & **SENet** & **RCA** & **RTA** & **TFA** & **FC** & **Average Accuracy** & **Standard Deviation** \\ \hline
1 & \(\surd\) & - & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & **99.80** & 0.02 \\
2 & \(\surd\) & - & \(\surd\) & - & - & \(\surd\) & 97.17 & 0.11 \\
3 & \(\surd\) & \(\surd\) & - & - & - & \(\surd\) & 93.09 & 0.10 \\
4 & \(\surd\) & - & - & - & - & \(\surd\) & 90.34 & 0.09 \\
5 & - & - & - & - & - & - & 81.5 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Identification results of different combination models.  
Figure 6: Gradient–weighted class activation mapping for different fault samples: (**a**) normal; (**b**) inner race fault; (**c**) outer race fault.  
Figure 8 shows the confusion matrix of the optimal model on the test set in the ten experiments of RTFANet. It can be seen that the accuracy of the test set is 99.86%, and only one sample with an inner ring fault is incorrectly identified as the normal state. In contrast, the other samples can be correctly identified. Therefore, it can be verified that the model has good generalization ability.