# Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on DRSN-GCE Model
## 4 Results
### Single Noise Experiment  
In the above noise experiments, we used Gauss and Laplace noise as input noises, but also added Gauss, Poisson, and Salt-and Pepper noises to the signal. These two experiments showed that adding Salt-and-Pepper noise and Poisson to the vibration signals reduced the accuracy of the models. The following experiments will be based on this model, and we will add Salt-and-Pepper noise and Poisson to the vibration signals as one input signal to investigate the impact of the two noise types on bearing fault diagnosis.  
The experiment yielded Table 11, which showed that a single Salt-and-Pepper noise caused more localized damage, making it harder for the model to extract effective features. This led to relatively poor diagnostic accuracy. The model is still able to perform well despite a Salt-and-Pepper noise. Its accuracy remains over 85%. We usually believe that noise in vibration signal processing will reduce the recognition of certain features. This, in turn, affects the accuracy. However, experimental results show that the simultaneous addition of Gauss noise, Salt-and-Pepper noise, and Poisson noise to the signal, on the contrary, achieves a higher recognition accuracy than when pretzel noise is added alone. From the perspective of noise type, Salt-and-Pepper noise is a sparse discrete noise, which is mainly manifested by the abrupt change of individual points in the signal, and it is very easy to destroy the continuity and local characteristics of the signal, whereas Gauss noise is a kind of additive continuous and widely distributed random noise [31]. When these two types of noise act together on the vibration signal, a \"complementary effect\" is formed to some extent [32], and the Gauss noise smooths out some of the extreme pretzel points to make the signal trend more coherent, which helps the model to extract effective features. From the perspective of signal preprocessing and feature extraction, the main components of the signal after multi-noise perturbation are still preserved when it is processed by filtering (e.g., continuous wavelet transform) or feature extraction, while the Salt-and-Pepper noise may be diluted in the frequency domain, which has less impact on the fault features. As a result, the model can focus more on stable, global information rather than being misled by local noise. The introduction of multiple noises not only effectively counteracts the local destructive effect of a single noise type on model training, but also enhances the model's ability to adapt to complex disturbed environments. This \"noise against noise\" strategy [33], to a certain extent, improves the accuracy of bearing fault diagnosis in the noise environment. The accuracy of this model is almost 100% in the Poisson environment. This indicates that it can deal well with vibration signals containing Poisson sound.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**SNR/dB**} & \multicolumn{2}{c}{**Gauss, Salt-and-Pepper, Poisson Noise**} & \multirow{2}{*}{**Salt-and-Pepper Noise**} & \multirow{2}{*}{**Poisson Noise**} \\  & & **Poisson Noise** & & \\ \hline \multirow{4}{*}{High-frequency} & \(-5\) & 89.11 \(\pm\) 0.11 & 88.35 \(\pm\) 0.12 & 100 \\  & \(-3\) & 90.21 \(\pm\) 0.10 & 89.11 \(\pm\) 0.13 & 100 \\  & \(-1\) & 91.88 \(\pm\) 0.11 & 88.11 \(\pm\) 0.12 & 100 \\  & \(1\) & 92.34 \(\pm\) 0.10 & 88.67 \(\pm\) 0.11 & 100 \\  & \(3\) & 93.67 \(\pm\) 0.10 & 88.56 \(\pm\) 0.10 & 100 \\ \hline \multirow{4}{*}{Low-frequency} & \(-5\) & 90.11 \(\pm\) 0.12 & 85.56 \(\pm\) 0.12 & 100 \\  & \(-3\) & 91.55 \(\pm\) 0.11 & 86.33 \(\pm\) 0.11 & 100 \\ \cline{1-1}  & \(-1\) & 92.77 \(\pm\) 0.11 & 86.44 \(\pm\) 0.13 & 100 \\ \cline{1-1}  & \(1\) & 93.38 \(\pm\) 0.10 & 87.56 \(\pm\) 0.11 & 100 \\ \cline{1-1}  & \(3\) & 93.97 \(\pm\) 0.11 & 88.78 \(\pm\) 0.11 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Diagnostic accuracy of this paper’s model under different noise conditions (%).