## 3 Experiments and Results
### Ablation Experiments  
#### 3.4.1 Different Time-Frequency Image Input  
TFCF images with rich time-frequency information are proposed as the input of our diagnostic model. The time-frequency images of STFT, TMSST, MSST and TFCF are input into RTFANet for experiments to verify their superiority. Considering that the time-frequency images of STFT, TMSST and MSST are single-channel images, they are directly copied and extended into dual-channel images to ensure the consistency of model parameters. Ten experiments are conducted for each input, the training samples are randomly selected for each experiment, and the diagnostic model is input to train until convergence. During the training of optimal model obtained from ten experiments, the loss of training set and accuracy of test set varies with the number of iterations, which are shown in Figure 5. In addition, the average accuracy and standard deviation of ten experimental results are recorded in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Network Layer** & **Nuclear Size** & **Step Length** & **Output Channel** & **Output Size** \\ \hline Input & - & - & - & \(2\times 400\times 800\) \\ Conv1 & \(5\times 5\) & 1 & 6 & \(6\times 396\times 796\) \\ ReLU & - & - & - & \(6\times 396\times 796\) \\ Max Pooling & \(2\times 2\) & 2 & - & \(6\times 198\times 398\) \\ RCA & - & - & 3; 6 & \(6\times 198\times 398\) \\ RTA & - & - & 99; 198 & \(6\times 198\times 398\) \\ RFA & - & - & 199; 398 & \(6\times 198\times 398\) \\ Conv2 & \(5\times 5\) & 1 & 16 & \(16\times 194\times 394\) \\ ReLU & - & - & - & \(16\times 194\times 394\) \\ Max Pooling & \(2\times 2\) & 2 & - & \(16\times 97\times 197\) \\ FC1 & - & - & 120 & \(1\times 1\times 120\) \\ FC2 & - & - & 84 & \(1\times 1\times 84\) \\ FC3 & - & - & 3 & \(1\times 1\times 3\) \\ Softmax & - & - & 3 & \(1\times 1\times 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: RTFANet model parameter settings.  
It can be seen from Figure 5 that no matter which time-frequency images are used as input. The model can converge to a small loss value eventually. It indicates that the model has a solid fitting ability, but the recognition accuracy of the test set is inconsistent at the end. It suggests that the information quality of images with different time-frequency is different, directly affecting the recognition results. Combined with Table 3, it can be seen that STFT has the worst effect, mainly because its time-frequency energy is too vague. MSST and TMSST compress the time-frequency energy, while the time-frequency information of some signal components is lost in compression, and the recognition effect is not good. While TFCF directly splices MSST and TMSST into a dual-channel image without eliminating any time-frequency information and achieves the highest average recognition accuracy.  
To further explore the reasons for the best effect of TFCF input into RTFANet, we investigate the gradient-weighted class activation mapping of three types of TFCF images [47]. As shown in Figure 6, it can be seen that regardless of the fault type, the SCRVF, the SCSVF, and the partial dispersion time-frequency information in the TFCF image, all contribute to the final decision of the model. Therefore, it is more advantageous to use a TFCF image with more time-frequency information as the input of the network model.