# Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on DRSN-GCE Model
## 4 Results
### Comparison Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frequency** & **Type of Model** & **Precision (Avg)** & **Recall (Avg)** & **F1 Scores (Avg)** & **Accuracy (Avg)** \\ \hline \multirow{6}{*}{high-frequency} & 1D-CNN & 78.89 \(\pm\) 0.16 & 77.67 \(\pm\) 0.15 & 76.47 \(\pm\) 0.15 & 76.33 \(\pm\) 0.15 \\  & CNN-Transformer & 82.51 \(\pm\) 0.15 & 82.11 \(\pm\) 0.15 & 81.91 \(\pm\) 0.14 & 82.01 \(\pm\) 0.15 \\  & 1D-DRSN & 85.56 \(\pm\) 0.15 & 85.11 \(\pm\) 0.14 & 85.19 \(\pm\) 0.15 & 85.55 \(\pm\) 0.14 \\  & Swin Transformer & 88.65 \(\pm\) 0.14 & 87.00 \(\pm\) 0.14 & 86.61 \(\pm\) 0.13 & 87.00 \(\pm\) 0.14 \\  & 1 DRSN-GCE & 86.60 \(\pm\) 0.13 & 86.44 \(\pm\) 0.14 & 86.43 \(\pm\) 0.13 & 86.11 \(\pm\) 0.13 \\  & Model of this paper & 89.25 \(\pm\) 0.12 & 89.01 \(\pm\) 0.11 & 89.08 \(\pm\) 0.11 & 89.11 \(\pm\) 0.11 \\ \hline \multirow{6}{*}{low-frequency} & 1D-CNN & 75.16 \(\pm\) 0.16 & 75.11 \(\pm\) 0.15 & 75.56 \(\pm\) 0.16 & 75.01 \(\pm\) 0.15 \\  & CNN-Transformer & 81.80 \(\pm\) 0.16 & 81.44 \(\pm\) 0.15 & 81.28 \(\pm\) 0.15 & 81.77 \(\pm\) 0.15 \\ \cline{1-1}  & 1D-DRSN & 84.45 \(\pm\) 0.15 & 84.22 \(\pm\) 0.14 & 84.05 \(\pm\) 0.15 & 84.33 \(\pm\) 0.14 \\ \cline{1-1}  & Swin Transformer & 86.48 \(\pm\) 0.14 & 86.22 \(\pm\) 0.15 & 86.32 \(\pm\) 0.15 & 86.22 \(\pm\) 0.13 \\ \cline{1-1}  & 1 DRSN-GCE & 85.82 \(\pm\) 0.13 & 85.56 \(\pm\) 0.13 & 85.62 \(\pm\) 0.14 & 85.65 \(\pm\) 0.13 \\ \cline{1-1}  & Model of this paper & 90.04 \(\pm\) 0.11 & 90.22 \(\pm\) 0.12 & 90.27 \(\pm\) 0.11 & 90.11 \(\pm\) 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance metrics for each model with a signal-to-noise ratio of \(-5\) (% or s). \begin{table}
\begin{tabular}{c c c c c c} \hline \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 & 76.33 \(\pm\) 0.15 & 78.32 \(\pm\) 0.16 & 80.55 \(\pm\) 0.17 & 82.33 \(\pm\) 0.16 & 85.77 \(\pm\) 0.15 \\ CNN-Transformer & 82.01 \(\pm\) 0.15 & 83.33 \(\pm\) 0.16 & 84.76 \(\pm\) 0.14 & 85.44 \(\pm\) 0.15 & 89.33 \(\pm\) 0.14 \\
1D-DRSN & 85.55 \(\pm\) 0.14 & 86.78 \(\pm\) 0.13 & 87.89 \(\pm\) 0.15 & 90.21 \(\pm\) 0.14 & 92.34 \(\pm\) 0.14 \\ Swin Transformer & 87.00 \(\pm\) 0.14 & 88.67 \(\pm\) 0.13 & 90.21 \(\pm\) 0.14 & 92.34 \(\pm\) 0.13 & 92.97 \(\pm\) 0.12 \\
1 DRSN-GCE & 86.11 \(\pm\) 0.13 & 87.64 \(\pm\) 0.14 & 88.78 \(\pm\) 0.13 & 91.22 \(\pm\) 0.14 & 92.55 \(\pm\) 0.12 \\ Model of this paper & 89.11 \(\pm\) 0.11 & 90.21 \(\pm\) 0.10 & 91.88 \(\pm\) 0.11 & 92.34 \(\pm\) 0.10 & 93.67 \(\pm\) 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Diagnostic accuracy of each model under three types of high-frequency noise (%). \begin{table}
\begin{tabular}{c c c c c c} \hline \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 & 75.01 \(\pm\) 0.15 & 77.53 \(\pm\) 0.17 & 79.82 \(\pm\) 0.16 & 82.54 \(\pm\) 0.16 & 86.21 \(\pm\) 0.15 \\ CNN-Transformer & 81.77 \(\pm\) 0.15 & 82.64 \(\pm\) 0.16 & 84.44 \(\pm\) 0.15 & 85.33 \(\pm\) 0.15 & 88.91 \(\pm\) 0.14 \\
1D-DRSN & 84.33 \(\pm\) 0.14 & 85.78 \(\pm\) 0.15 & 87.86 \(\pm\) 0.15 & 90.01 \(\pm\) 0.14 & 91.55 \(\pm\) 0.15 \\ Swin Transformer & 86.22 \(\pm\) 0.13 & 87.75 \(\pm\) 0.14 & 88.37 \(\pm\) 0.14 & 90.34 \(\pm\) 0.13 & 82.37 \(\pm\) 0.14 \\
1 DRSN-GCE & 85.65 \(\pm\) 0.13 & 86.55 \(\pm\) 0.14 & 88.83 \(\pm\) 0.13 & 89.76 \(\pm\) 0.14 & 91.86 \(\pm\) 0.14 \\ Model of this paper & 90.11 \(\pm\) 0.12 & 91.55 \(\pm\) 0.11 & 92.77 \(\pm\) 0.11 & 93.38 \(\pm\) 0.10 & 93.97 \(\pm\) 0.11 \\ \hline \hlineThe outputs of each model are visualized using t-SNE at a signal-to-noise ratio of \(-5\) dB. The graph (d), in Figure 8, shows that this model is superior to other deep learning models in clustering fault features and identifying bearings of different health states. It also provides a more accurate classification.