# Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on DRSN-GCE Model
## 4 Results
### Introduction to the Experimental Dataset  
In this paper, a generally recognized public bearing dataset is used for experimental analysis, which makes the proposed model instructive for fault diagnosis of rolling bearings under actual working conditions. The Case Western Reserve University (CWRU) experimental setup is shown in Figure 3. The experimental rig consists of a 2 hp motor, dynamometer, and torque transducer. The dataset was collected using SKF6205-2 RSjEM bearings at sampling frequencies of 12 kHz and 48 kHz, and a single point of failure was set up by EDM. For signal acquisition, the experimental platform was fitted with piezoelectric accelerometers (mainly PCB Piezotronics type 352C33 accelerometers), which were mounted on the drive end bearing cover (DE, Drive End) and the fan end (FE, Fan End) of the motor, and held in place by means of magnetic bases. The acquired signals were captured by a National Instruments (NI) data acquisition card and transferred to a computer for processing. The types of bearing failures include three types: rolling element failures, inner ring failures, and outer ring failures. Each failure type contains three different levels of damage diameters, and we divided the data into ten categories: one category represents normal operating conditions, and the remaining nine categories cover failure conditions. Rolling body failures with failure diameters of 0.1778 mm, 0.3556 mm, 0.5334 mm (labeled BA_I, BA_II, and BA_III, respectively), inner ring failures with failure diameters of 0.1778 mm, 0.3556 mm, 0.5334 mm \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Quantities** & **Parameters (Convolution Kernel Size, Number of Pass-In** \\  & & **Channels, Number of Output Channels, Step Size)** & **Output Size** \\ \hline Conv & 1 & (3, 1, 16, 1) & 16 \(\times\) 16 \(\times\) 16 \\ Block1 & 1 & (3, 16, 32, 1) & 32 \(\times\) 8 \(\times\) 8 \\ Block2 & 1 & (3, 32, 64, 1) & 64 \(\times\) 4 \(\times\) 4 \\ Block3 & 1 & (3, 64, 64, 1) & 64 \(\times\) 4 \(\times\) 4 \\ Block4 & 1 & (3, 64, 64, 1) & 64 \(\times\) 4 \(\times\) 4 \\ Flatten & 1 & / & 1024 \\ FC1 & 1 & (1024, 256) & 256 \\ FC2 & 1 & (256, num\_classes) & num\_classes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Structural parameter settings. (labeled IR_I, IR_II, and IR_III, respectively), and outer ring failure diameters of 0.1778 mm, 0.3556 mm, 0.5334 mm (labeled OR_I, OR_II, and OR_III, respectively) were investigated. In this study, we constructed the dataset using vibration signals from the drive-end (DE) bearings with noise artificially added to the vibration signals to simulate noise-containing signals in real industrial environments, sampled at 12 kHz with a rotational speed of 1730 rpm. Each bearing condition includes 300 samples, totaling 3000 samples, and each sample contains 2048 data points. Dividing the training set and validation set according to the ratio of 7:3, 2100 training samples and 900 validation samples can be obtained. The dataset details are shown in Table 2.  
To simulate the vibrations that contain noise, we added different noise types to the vibration signals to simulate this noise. In the next experiments, we added noise to simulate the noise-containing vibration signal. Based on other research in the field of bearing diagnostics in noisy environments, we also added other non-Gauss noises to the vibration signals to make them more consistent with real industrial scenarios. For example, Lapace noise is a more complex and randomized non-Gauss noise in the real industrial scene. Salt-and-Pepper noise is also a common non-ideal noise simulation in the field of rolling bearing fault diagnosis. There is also Poisson noise, a typical non-Gauss noise. We set different signal-to-noise ratio (SNR) levels for each noise type to simulate various noise intensities, as shown in Table 3. The noise-laden vibration signals are then subjected to a continuous wavelet transform to generate a two-dimensional colored wavelet time-frequency plot, as shown in Figure 4.