# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 4 Experimental Verification
### Case 2:PUI Bearing Dataset  
The PU bearing dataset features the 6203 bearing model, encompassing both artificial and real damage types. Figure 15 displays the test bench setup, which consists of a drive motor, a torque measurement shaft, a testing module and a load motor. A piezoelectric accelerometer is employed to gather the vibration signals from the bearing pedestal, with the sampling frequency set at 64 kHz. By adjusting the speed of the drive system, the radial force on the bearing and the load torque affecting the drive system are tested.  
In the PU bearing dataset, we chose the subset recorded under operating conditions of 1500 rpm, with a 0.7 Nm load and a radial force of 1000N. In the bearing fault dataset, each fault type is represented by 100 generated samples. Using the same overlapping sampling method of 512 points as in Case 1, we slice 1024 data points with a fixed length  
Figure 14: Confusion matrix of different networks on dataset D under Case 1.  
Figure 15: PU test bench.  
and distribute them into training and testing sets in a 2:8 ratio. Table 6 displays the specific fault types and their quantities within the dataset.  
The fault signal is broken down into \(k\) IMF components through VMD. The value of \(k\) is set using the same center frequency method as outlined in Case 1. As shown in Table 7, when \(k\) is 9 and \(k\) is 8, the frequency overlaps, so \(k\) is taken as 8. When \(k\) is 8, it provides a wide frequency coverage, and the frequency points are evenly distributed, which more effectively represents the important frequency components of the signal. As shown in Table 8, using the same method as in Case 1, select the best first six IMF components by averaging the kurtosis of each IMF component. Finally, each IMF component is successively generated into a picture through SDP transformation, and the SDP parameter selection is similar to Case 1. The generated fault image is shown in Figure 16.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Code** & **Fault Type** & **Label** & **Fault Source** & **Train** & **Test** \\ \hline K001 & N & 0 & None & 20 & 80 \\ KA01 & OR1 & 1 & Artificial damage & 20 & 80 \\ KA04 & OR2 & 2 & Real damage & 20 & 80 \\ KB23 & OR2+IR2 & 3 & Real damage & 20 & 80 \\ KI01 & IR1 & 4 & Artificial damage & 20 & 80 \\ KI14 & IR2 & 5 & Real damage & 20 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detailed composition of dataset under Case 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(k\) & \multicolumn{8}{c}{**Center Frequency/HZ**} \\ \hline
5 & 0.0552 & 0.0908 & 0.1524 & 0.2857 & 0.4299 & & & \\
6 & 0.0527 & 0.0712 & 0.1508 & 0.2093 & 0.3346 & 0.4447 & & \\
7 & 0.0250 & 0.0574 & 0.1508 & 0.1301 & 0.2476 & 0.3417 & 0.4478 & & \\
8 & 0.0244 & 0.0567 & 0.0925 & 0.1525 & 0.2118 & 0.2883 & 0.3547 & 0.4543 & \\
9 & 0.0242 & 0.0566 & 0.0922 & 0.1525 & 0.2104 & 0.2794 & 0.3374 & 0.4035 & 0.4646 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Center frequencies of different \(k\) values under Case 2.  
Figure 16: Fault diagram of VSDP under Case 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **IMF1** & **IMF2** & **IMF3** & **IMF4** & **IMF5** & **IMF6** & **IMF7** & **IMF8** \\ \hline Kurtosis average & 6.9350 & 6.5255 & 5.2420 & 11.1878 & 7.8981 & 12.6360 & 13.5512 & 12.7305 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average kurtosis of each IMF component under Case 2.