## 4 Analysis and Discussion  
Gaussian white noise is added to the original signal in order to meet the actual condition. The SNR is \(-10\) dB, \(-8\) dB, \(-6\) dB, \(-2\) dB, and \(2\) dB. The model's reliability under noise conditions is verified. The CNN convolutional layer can extract features. Under complex conditions, the features to be extracted are increasingly complex, so it is necessary to increase the number of convolution layers to improve the model's generalization ability. However, with the increase in convolution layers, the learning difficulty of the model increases. In this paper, wavelet layers are used to combine different convolution layers, and the convolution layers are 1, 2, 4, 6, and 8. From Table 5, it can be seen that under different noise conditions, with the increase in convolution layers, the accuracy rate increases. Overall, the accuracy rate increases first and then maintains a small fluctuation. When there are four convolution layers, the comprehensive performance of the model is optimal. To further analyze the model's performance, CNNs, LeNets, and ResNets were selected for comparison with the proposed model. The diagnostic results for different signal-to-noise ratios are shown in Figure 16. As observable from the figure, as the noise increases, the diagnostic accuracy decreases. The accuracy of CNNs and ResNets is 60% and 63%, respectively, when the SNR is \(-10\), and that of the present method was 10% and 3%. In conclusion, the proposed model has better anti-noise performance. Figure 16: Comparison of results under different noises. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**SNR/dB**} \\ \hline  & \(-\mathbf{10}\) & \(-\mathbf{8}\) & \(-\mathbf{6}\) & \(-\mathbf{2}\) & \(\mathbf{2}\) \\ \hline
1 & 72.35\% & 84.50\% & 91.02\% & 98.80\% & 98.35\% \\
2 & 73.26\% & 85.31\% & 91.87\% & 99.05\% & 99.75\% \\
4 & 75.28\% & 88.69\% & 93.52\% & 99.43\% & 99.89\% \\
6 & 75.77\% & 88.45\% & 93.48\% & 99.00\% & 99.69\% \\
8 & 75.60\% & 88.52\% & 93.11\% & 98.31\% & 98.88\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results of different convolutional layers. In order to further verify the generalization ability of the model, the XJTU-SY bearing data set was selected, which contained fault vibration signals of 15 rolling bearings under three different operating conditions. The test bearing was LDK UER204 rolling bearing. See Table 6 for its relevant parameters. Three types of working conditions were designed for the test, as shown in Table 7. There are five bearings under each type of working condition. Table 8 shows XJTU-SY bearing data set information. From Table 9, it can be seen that under different noise conditions, with the increase in convolution layers, the accuracy rate increases. Overall, the accuracy rate increases first and then maintains a small fluctuation. When there are four convolution layers, the comprehensive performance of the model is optimal. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Numerical** & **Parameter Name** & **Numerical** \\ \hline Inner ring diameter/mm & 29.30 & Ball diameter/mm & 7.92 \\ Outer ring diameter/mm & 39.80 & Number of balls & 8 \\ Center diameter/mm & 34.55 & Contact angle/(\({}^{\circ}\)) & 0 \\ Dynamic load rating/N & 12,820 & Static load \\  & & rating/kN & 6.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: LDK UER204 bearing parameters.