# Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples
## 4 Experimental Verification
### Case 2: Bearing Dataset with Two Missing Failure Samples  
The dataset used in this section is still from Case Western Reserve University, the difference is that in order to further verify the efficiency of the proposed method, increasing the number of emulated signals. 200 samples are also randomly selected from each health state, then the envelope signal of the sample is obtained. Half of the samples are used as training data and the other half as test data. The details of the Dataset C are listed in Table 4. This section selects the inner ring fault and outer ring fault as the missing fault samples, and the missing fault samples with simulated signals are replaced for the experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Fault Location** & \
\begin{tabular}{c} **Signal Source** \\ **Dataset C** \\ \end{tabular} &
\begin{tabular}{c} **Sample Size** \\ **(Train/Test)** \\ \end{tabular} \\ \hline NC & Normal & Measurement & 100/100 \\ RF & Ball & Measurement & 100/100 \\ IF & Inner Race & Simulation & 100/100 \\ OF & Outer Race & Simulation & 100/100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Situation of Dataset C.  
Figure 11: Dimensionality reduction visualization results on Dataset A: (a) GAN, (b) WGAN. (c) WGAN-GN.  
Figure 10: Accuracies of the two datasets using the three methods: (a) Dataset A, (b) Dataset B.  
Diagnosis Results  
Figure 13 gives the comparison result of Dataset C. It is obvious that the trends of the generated samples and the raw samples are very similar, and the features in the generated samples have basically the same distribution as the features in the raw samples. By contrasting the generated samples with the original samples of 3D plots, RF samples are chosen to investigate the feature extraction capability of WGAN-GN. According to Figure 14, the simulated signals generated by WGAN-GN has nearly identical properties to the original samples.  
Figure 12: Dimensionality reduction visualization results on dataset B: (**a**) GAN. (**b**) WGAN. (**c**) WGAN-GN.  
Figure 13: Comparison between real and generated signals for dataset C: (**a**) Raw signals, (**b**) Generated signals.  
Figure 15 displays the findings of the diagnosis accuracy test. For Dataset C, the average accuracies of the three methods are 81.37%, 90.00%, and 96.48%, respectively, with 1.61%, 1.01%, and 0.85% as their standard deviations. The proposed approach continues to offer maximum degree of diagnostic accuracy when compared to other methods. Furthermore, Figure 16 displays the outcomes of the dimension reduction. It is concluded that the proposed model greatly outperforms them in terms of clustering and classification compared to the other two methods. It also proves that the obtained simulation signal can replace the real measured signal to a certain extent for fault diagnosis at the same time.  
Figure 14: Extracted features of generator for RF samples: (**a**) The first layer, (**b**) The second layer, (**c**) The third layer, (**d**) The output layer, (**e**) The raw sample.  
Figure 15: Accuracies of the datasets C using the three methods.