# Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on DRSN-GCE Model
## 4 Results
### Comparison Test  
For fault classification identification in the case of rolling bearing vibration signals affected by noise, in addition to Gauss noise, there are Laplace noise, Salt-and-Pepper noise, Poisson noise, and so on. In order to simulate the actual industrial environment, this paper introduces these noises as interferences, which are used to evaluate the robustness of the diagnostic model under various types of noise interferences. In order to verify the effectiveness of the model in this paper, we compared it with other deep learning models (1D-CNN, CNN-Transformer, Swin Transformer, 1D-DRSN, 1 DRSN-GCE). Both Gauss noise and Laplace noise (non-Gauss noise) were added to the vibration signal to obtain the mean and standard deviation of the diagnostic accuracy, precision, recall, F1 score of each batch of models with different signal-to-noise ratios. Tables 5-7 show the results. In Table 5, we can see that this model's diagnostic accuracy is best at signal-to-noise ratios between \(-5\) dB and \(3\) dB. In Table 5, we can see that this model's diagnostic accuracy reaches \(99.22\%\) with a signal-to-noise ratio of \(-5\). According to Table 5, the accuracy of other models is \(78.81\), \(90.89\%\), \(95.11\%\), \(96.89\%\), and \(95.44\%\). This paper's diagnostic accuracy is \(4.11\%\) greater than the 1D-DRSN model, while at the same it is \(3.78\%\) more accurate than the 1 DRSN-GCE model without CWT. CWT also makes it possible to better diagnose and classify the signals by converting a one-dimensional signal of time into a two-dimensional image. In Table 6, the paper's accuracy is the best in low-frequency conditions. This shows that the model has the ability to cope with noise frequency variations. In Table 7, the performance of this model is superior to other models, in terms such as precision, recall, and F1 score. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Type of Model**} & \multicolumn{5}{c}{**SNR/dB**} \\ \cline{2-6}  & \(-\mathbf{5}\) & \(-\mathbf{3}\) & \(-\mathbf{1}\) & \(\mathbf{1}\) & \(\mathbf{3}\) \\ \hline
1D-CNN & \(80.21\pm 0.14\) & \(82.75\pm 0.15\) & \(85.37\pm 0.15\) & \(89.45\pm 0.16\) & \(92.63\pm 0.15\) \\ CNN-Transformer & \(91.88\pm 0.15\) & \(93.51\pm 0.14\) & \(93.79\pm 0.16\) & \(96.54\pm 0.15\) & \(97.55\pm 0.14\) \\
1D-DRSN & \(93.01\pm 0.14\) & \(94.55\pm 0.15\) & \(95.77\pm 0.15\) & \(96.83\pm 0.13\) & \(97.33\pm 0.14\) \\ \hline \end{tabular}
\end{table} 
Table 6: Diagnostic accuracy of each model under low-frequency noise (%). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SNR/dB** & **Method 1** & **Method 2** & **Method 3** & **Method 4** & **Method 5** & **Method 6** \\ \hline \(-5\) & \(87.88\pm 0.15\) & \(93.19\pm 0.14\) & \(94.38\pm 0.14\) & \(99.34\pm 0.12\) & \(98.67\pm 0.15\) & \(95.35\pm 0.17\) \\ \(-3\) & \(88.33\pm 0.13\) & \(92.35\pm 0.13\) & \(94.77\pm 0.14\) & \(99.56\pm 0.11\) & \(98.78\pm 0.15\) & \(96.57\pm 0.16\) \\ \(-1\) & \(89.11\pm 0.14\) & \(93.33\pm 0.15\) & \(95.25\pm 0.15\) & \(99.77\pm 0.12\) & \(98.88\pm 0.14\) & \(97.44\pm 0.15\) \\ \(1\) & \(88.77\pm 0.16\) & \(93.81\pm 0.13\) & \(95.67\pm 0.14\) & \(99.89\pm 0.10\) & \(99.32\pm 0.16\) & \(97.88\pm 0.16\) \\ \(3\) & \(90.22\pm 0.15\) & \(93.77\pm 0.14\) & \(96.88\pm 0.13\) & \(99.97\pm 0.01\) & \(99.87\pm 0.12\) & \(96.55\pm 0.15\) \\ \hline \end{tabular}
\end{table} 
Table 4: Diagnostic accuracy under different methods (%).