## 4 Validation of the Proposed Method
### Case 2: Experiment Results on the Jiang Nan University Bearing Dataset and Performance Analysis  
#### 4.3.1 Data Collection  
The Jiang Nan University dataset was collected from a centrifugal fan system rolling bearing fault diagnosis test rig [36]. Figure 10 displays the experimental setup for rolling bearing fault diagnosis. The experimental bench used a PCB MA352A60 acceleration sensor to collect the vibration signals in the vertical direction. The speed was 1000 r/min, and the sampling frequency was set to 50 kHz. This study considered four bearing health conditions, including outer-race fault, inner-race fault, roller element fault and normal. Bearings under each health condition were operated at 800 rpm rotating speeds. These fault bearings are shown in Figure 11. In this experiment, the data set contained 2000 groups of training data, and each training sample contained 2400 sampling points. The standardized fault samples under each fault state were randomly divided into training data set and test data set with the scale of 7:3. We selected the same amount of data under each operating condition, 350 samples \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Data** & **Model** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Average** \\ \hline \multirow{2}{*}{A} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 92.33 & 92 & 92.33 & 92.33 & 92 & 92 & 92.33 & 92 & 92.33 & 92.23 & **92.198** \\  & WPT-INPSO-SVM & 80 & 80 & 80 & 80 & 80 & 80 & 80 & 80 & 80 & 80 & 80 \\ \hline \multirow{2}{*}{B} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 93.33 & 93.33 & 92.67 & 93.33 & 93.33 & 92.67 & 93.33 & 91 & 92.67 & 93.67 & **92.933** \\  & WPT-INPSO-SVM & 83.67 & 83.67 & 84 & 83.67 & 83.67 & 83.67 & 83.67 & 83.67 & 83.67 & 83.67 & 83.703 \\ \hline \multirow{2}{*}{C} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 93.33 & 93.67 & 93.67 & 93.33 & 94 & 93.67 & 93.67 & 94 & 93.67 & 93.67 & **93.668** \\  & WPT-INPSO-SVM & 85 & 84.67 & 85 & 85 & 85 & 84.67 & 85 & 84.67 & 85 & 84.688 \\ \hline \multirow{2}{*}{D} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 94.33 & 93 & 94.33 & 94.33 & 93.67 & 91.67 & 94.33 & 92 & 91 & 91.33 & **92.999** \\  & WPT-INPSO-SVM & 85 & 85.33 & 85.33 & 85 & 85.33 & 85.33 & 85.33 & 85.33 & 85.33 & 85.264 \\ \hline \multirow{2}{*}{E} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 95.67 & 91.33 & 96 & 96 & 95.33 & 95.33 & 95.33 & 95.33 & 96 & **95.165** \\  & WPT-INPSO-SVM & 83 & 82.67 & 83 & 83 & 83 & 83 & 83.67 & 83 & 83 & 83 & **82.934** \\ \hline \multirow{2}{*}{F} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 90 & 88.67 & 89.33 & 90 & 89.67 & 90.33 & 90 & 90 & 90.33 & 90.33 & **89.866** \\  & WPT-INPSO-SVM & 83.67 & 83.67 & 83.37 & 83.33 & 83.33 & 83.67 & 83.67 & 83.33 & 83.67 & 83.568 \\ \hline \multirow{2}{*}{G} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 97.67 & 97.67 & 97.33 & 97.67 & 98 & 97.33 & 98 & 97.67 & 97.67 & **97.67** & **97.668** \\  & WPT-INPSO-SVM & 82.33 & 82.67 & 82 & 82.33 & 82.33 & 82.67 & 83 & 83 & 82.566 \\ \hline \multirow{2}{*}{H} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 92.33 & 92.33 & 91.67 & 92.33 & 92.33 & 92.33 & 92.33 & 92.33 & 92.23 & **92.264** \\  & WPT-INPSO-SVM & 85 & 85 & 85 & 85 & 85 & 85 & 85 & 85 & 85 & 85 \\ \hline \multirow{2}{*}{I} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 95 & 95 & 95.67 & 95.33 & 96 & 95.33 & 96 & 95 & 95.33 & **95.366** \\  & WPT-INPSO-SVM & 83.33 & 88 & 83.33 & 83.33 & 83.33 & 83.33 & 83.33 & 83.33 & 83.33 & **83.797** \\ \hline \multirow{2}{*}{J} & \multicolumn{1}{c}{1 DCNN-INPSO-SVM} & 92.67 & 92.67 & 92 & 92.67 & 92.67 & 92.33 & 92.33 & 92.67 & 92.67 & 92.33 & **92.501** \\  & WPT-INPSO-SVM & 79.67 & 79.67 & 79.67 & 79.67 & 79.67 & 79.67 & 79.67 & 79.67 & 79.67 & **79.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recognition results of ten trials using different methods. Figure 10: Experimental setup for the rolling bearing fault diagnosis. were used as the training set and 150 samples were used as the test set.