## 4 Experimental Study
### Data Preprocessing  
The original oscillation signal collected in the experiment is shown in Figure 9. It can be clearly seen that there are 6 mutations (MFL signals) in the figure below, corresponding to 6 kinds of defects in Figure 6. However, it is very difficult to distinguish the different faults in the time domain. In order to realize the identification of various broken wires, each MFL signal with 1024 data points is cut into a data segment. Then, these data segments are transformed into time-frequency images by continuous wavelet transform (CWT). The conversion is implemented in MATLAB through the \"cwtfilterbank\" function, where the analytic Morse wavelet is used in the filter bank. The conversion result is shown in Figure 10.  
The  
The training set and the generated data are used to train the CNN. After the training, the testing set is used to test the effectiveness of fault recognition. As shown in Figure 12, a confusion matrix is employed to visualize the fault classification effect of the proposed method. Obviously, almost all faults have been correctly identified, the recognition accuracy is 99.3%.  
In addition, the proposed method is compared with the back propagation (BP) neural network and the traditional CNN. For the BP network, some artificial features are extracted from MFL signals (Figure 9) for training. For the conventional CNN, the training set in this paper is used for training without generating data, and the testing set is used for identification. The accuracy of each fault and the total identification accuracy are revealed in Figure 13. Obviously, there is a fluctuation in the identification accuracy of different fault  
Figure 11: Fake time-frequency images generated by GAN: (**a**) 1 external broken wire; (**b**) 2 external broken wires; (**c**) 3 external broken wires; (**d**) 1 internal broken wire; (**e**) 2 internal broken wires; (**f**) 3 internal broken wires.  
Figure 10: The time-frequency images converted by MFL signals: (**a**) 1 external broken wire; (**b**) 2 external broken wires; (**c**) 3 external broken wires; (**d**) 1 internal broken wire; (**e**) 2 internal broken wires; (**f**) 3 internal broken wires.  
cases. However, the proposed method has the highest classification accuracy for each fault. Compared with the BP network and the traditional CNN, the GAN+CNN method has the best recognition accuracy.  
In order to show the powerful ability of the proposed GAN-based model in broken wire recognition, the features learned by the softmax layer of the CNN are represented by t-distributed stochastic neighbor embedding (t-SNE), which is compared with manual features in the BP and a single CNN, as shown in Figure 14. It can be seen from Figure 14 that artificial features failed to cluster different faults, and a serious coverage occurs between different broken wire samples. For a single CNN, the clustering of faults is more obvious, and only a few errors occur. For the proposed GAN+CNN method, the boundaries of  
Figure 12: The confusion matrix using the proposed method.  
Figure 13: The comparison of each fault and the total identification accuracy of different methods.  
different faults are more clearly distinguished. At the same time, compared with the other two methods, the coverage phenomenon of various fault samples is lowest using the proposed method.