## 4 Experimental Verification
### Analysis of Experimental Results and Visualization of Training Classification Process  
In this section, five sets of comparison networks are introduced to verify the feasibility of the method proposed in this paper through experimental results. The five sets of comparison network models are as follows: WDCNN + original vibration data, WDCNN + FFT signal processing, Propose + original vibration data, SVM + FFT (where SVM uses Gaussian kernel function and the error penalty term coefficient is taken as 1), and ANN + FFT (the number of neurons in each layer is 1000, 500, 300, 100, 50, and each layer uses a RELU activation function and l2 regularization). The first three sets of comparison networks are used as self-comparison experiments of the proposed network model. This paper presents a comparison with the original WDCNN network, which was the leading deep convolutional network-based bearing fault diagnosis method at that time, containing five convolutional layers and one fully connected layer. ANN and SVM are two traditional machine learning methods used for bearing fault diagnosis. ANN learns by training mechanical fault information and diagnosis experience, and then expresses the learned fault diagnosis knowledge using connection weights distributed inside the network. SVM has the advantage of solving small samples, nonlinear data, and strong generalization ability. To avoid the randomness of the experiments and to ensure the credibility of the experimental comparisons, 10 repetitions of the experiments were undertaken for each model. Through the data enhancement technique, each experiment randomly grabs the divided bearing dataset under three working conditions to constitute different training sets, validation sets, and test sets to verify the reliability of the model and eliminate the influence of experimental randomness. The mean and standard deviation of the 10 experimental results were taken as the error range of the experimental results. The results are shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Working Condition I**} \\ \hline
**Fault Location** & **Cage** & **Inner-Outer** & \multicolumn{6}{c}{**Normal**} & \multicolumn{6}{c}{**Outer-Race**} \\ \hline Type Label & 0 & 1 & & & 2 & & & 3 \\ \hline Bearing & 1\_4 & 1\_5 & 1\_1 & 1\_2 & 1\_3 & 1\_4 & 1\_5 & 1\_1 & 1\_2 & 1\_3 \\ Training Set & 7000 & 7000 & 1400 & 1400 & 1400 & 1400 & 1400 & 2333 & 2333 & 2333 \\ Validation Set & 1000 & 1000 & 200 & 200 & 200 & 200 & 333 & 333 & 334 \\ Test Set & 2000 & 2000 & 400 & 400 & 400 & 400 & 400 & 667 & 667 & 666 \\ \hline \hline \multicolumn{10}{c}{**Working condition II**} \\ \hline
**Fault location** & **Inner-race** & **Outer-race** & \multicolumn{6}{c}{**Normal**} & \multicolumn{6}{c}{**Cage**} \\ \hline Type Label & 0 & 1 & & 2 & & 3 & 3 \\ Bearing & 2 4 & 2 5 & 2 1 & 2 2 & 2 3 & 2 4 & 2 5 & 2 1 & 2 2 & 2 3 \\ Training Set & 7000 & 7000 & 1400 & 1400 & 1400 & 1400 & 2333 & 2333 & 2333 \\ Validation Set & 1000 & 1000 & 200 & 200 & 200 & 200 & 333 & 333 & 334 \\ Test Set & 2000 & 2000 & 400 & 400 & 400 & 400 & 400 & 667 & 667 & 666 \\ \hline \hline \multicolumn{10}{c}{**Working condition III**} \\ \hline
**Fault location** & **Inner-race** & \multicolumn{6}{c}{**Normal**} & \multicolumn{6}{c}{**Outer-race**} & \multicolumn{6}{c}{**Inner-outer-cage**} \\ \hline Type Label & 0 & & & 1 & & 2 & 3 \\ bearing & 3\_3 & 3\_4 & 3\_1 & 3\_2 & 3\_3 & 3\_4 & 3\_5 & 3\_1 & 3\_2 & 3\_2 \\ Training Set & 3500 & 3500 & 1400 & 1400 & 1400 & 1400 & 3500 & 3500 & 7000 \\ Validation Set & 500 & 500 & 200 & 200 & 200 & 200 & 200 & 500 & 500 & 1000 \\ Test Set & 1000 & 1000 & 400 & 400 & 400 & 400 & 400 & 1000 & 1000 & 2000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Description of the experimental dataset. It can be seen from Figure 11 that the network accuracy reflected by the same network model is very different under different working condition data.