# Fault Diagnosis of Induction Motor Using Convolutional Neural Network
## 4 Experiment and Results  
The simulation and experiment environments were implemented using Python 3.5 and the Keras library in Windows 10 OS. To verify the performance of the proposed induction motor fault diagnosis system, vibration signals obtained from the induction motors shown in Figure 1 were used as the test data. The composition of the proposed CNN model was as follows. Convolutional layer 1 consisted of 64 filters with a size of \(5\times 5\), and convolutional layer 2 consisted of 32 filters with a size of \(3\times 3\). The max pooling layer measured \(2\times 2\) in size. The fully connected layer consisted of 256 nodes, and the output layer consisted of three nodes. The number of CNN inputs was 1024, and the number of CNN outputs was three. To test the performance of the proposed induction motor fault diagnosis system, vibration signal data were acquired using a vibration sensor by artificially generating normal, rotor fault, and bearing fault states in the experimental apparatus, as shown in Figure 6(a)-c, respectively. Table 2 shows the numbers of normal, rotor fault, and bearing fault data used for training and testing. A total of 200 datasets were used to train the CNN, and 100 datasets were used to test the CNN. Each dataset contained 1024 data samples. The parameter settings for CNN training were as follows: \(\eta=0.001\), \(\varepsilon=10^{-8}\), iteration epoch for CNN training = 30. The vibration data used for training the CNN were not subjected to frequency domain transformation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Induction Motor State** & **Number of Learning Datasets** & **Number of Test Datasets** \\ \hline Normal & 200 & 100 \\ Rotor fault & 200 & 100 \\ Bearing fault & 200 & 100 \\ \hline Total & 600 & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The dataset for simulation. Figure 7: Experimental data. Figure 8 shows the test results obtained in two cases of fault diagnosis when the inverter changes from the normal state to the rotor fault state and when the inverter changes from the normal state to the bearing fault state. In Figure 8, the x-axis represents the number of vibration data, and the y-axis represents the fault class. Classes 1, 2, and 3 represent the normal, rotor fault, and bearing fault states. A total of 300 datasets were used in the test, including 100 datasets each for the normal, rotor fault, and bearing fault states. The simulation proceeded as follows. First, after inputting 100 pairs of normal data, rotor fault data and bearing fault states were input. Figure 8 shows the simulation results obtained using the rotor fault data. Figure 8 shows misclassification of the simulation data of the normal state as rotor fault state; two instances of this misclassification occurred in total. Figure 8 shows misclassification of the rotor fault state simulation data as normal state; two instances of this misclassification occurred in total. Figure 8 shows the simulation results of bearing fault data. All bearing fault test data were classified correctly. Accordingly, as shown in Table 3, the accuracies of fault diagnosis of the normal, rotor fault, and bearing fault states are 98%, 98%, and 100%, respectively. In addition, we demonstrated the use GUI to determine whether an induction motor has a rotor fault or bearing fault, as in Figures 9 and 10, respectively. Figure 8 shows the result of rotor fault diagnosis of an induction motor in a real environment. Figure 9 shows the induction motor with the proposed fault diagnosis system is shown in Figure 9. Figure 10 shows the diagnosis result of bearing fault of an induction motor in a real environment. Figure 10 shows the induction motor with a bearing fault used in the test, and Figure 10 shows the bearing fault in the LED, indicating the output of the fault diagnosis system. The experimental results show that the proposed fault diagnosis system can accurately diagnose faults even in a real environment. Figure 8: Result of induction motor fault diagnosis in simulation