## 5 Identification Result of Motor Fault Type
### Results of Induction Motor Fault Classification  
First, the features of the measurement signal were extracted by HHT. The number of features was the most, but there may be more features that cannot clearly distinguish the type of failure, so that the  
Figure 11: (**a**) Feature distribution of motor failure (\(\infty\)dB); (**b**) feature distribution of motor failure (\(20\) dB). Figure 10: Architecture diagram of Hilbert–Huang transform (HHT) extraction features. accuracy could only reach 88.26%. Second, through ReliefF feature-selection method, features with lower feature weights could be deleted, removing 72% of the total number of features. If FCBF were used, it could be divided into two stages for screening features. In the initial stage, comparing the SU values of features and fault types, most of the features with lower impact could be deleted, removing 76% of the total number of features. Later, through the feature-to-feature correlation screening method, FCBF could effectively delete redundant features. A total of 87.5% of the total features can be deleted, as shown in Table 3. In order to prove that FCBF also had the advantage of running time, this study also calculated the time (Run 200 times to get the methods average running time). The result showed that FCBF had the ability to achieve a fast identification than ReliefF and SU, as shown in Table 4. In summary, the feature-selection method FCBF could delete the most features after the selection, which was the best for the three kinds of feature-selection method. In this study, BPNN was used to classify each fault condition of the motor and white noise of 40 dB, 30 dB and 20 dB was added for research. It can be observed from Table 5 that in the absence of noise, the accuracy of HHT was 88.26% and the accuracy using HHT combined with ReliefF, SU value and FCBF were 90.05%, 90.02% and 90.75%. Among the three methods, FCBF could obtain better accuracy. Using HHT combined with FCBF-PSO particle group to optimize the weight of the feature, the accuracy could be increased from 88.26% to 92.85%. Therefore, it could be shown that the FCBF-PSO method proposed in this research could delete fewer important features and give the corresponding weights to the selected features after optimization. As seen in Table 6, a slight white noise SNR = 40 dB is added to the signal. The accuracy of HHT is 87.25% and after effectively removing unnecessary features through three feature-selection methods, the recognition results obtained are 88.35%, 85.87% and 88.86%. Explain that after screening, these three methods can also maintain the accuracy. Finally, using the method of FCBF-PSO proposed in this research to identify the fault condition, the results show that the accuracy can be increased from 87.25% to 91.76%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **HHT** & **ReliefF** & **SU** & **FCBF** \\ \hline \(\infty\) dB & 0.413 & 0.336 & 0.334 & **0.311** \\
40 dB & 0.418 & 0.345 & 0.335 & **0.320** \\
30 dB & 0.407 & 0.358 & 0.339 & **0.326** \\
20 dB & 0.413 & 0.373 & 0.345 & **0.330** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Running time (in seconds) of the three feature selection algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Normal** & \multicolumn{4}{c}{**Accuracy (BPNN\%)**} \\ \hline
**Method** & **Normal** & **Bearing** & **Rotor** & **Stator** & **Average** \\ \hline HHT & 72.44 & 95.71 & 94.01 & 90.9 & **88.26** \\ ReliefF & 75.64 & 93.9 & 96.76 & 94.03 & **90.05** \\ SU & 75.67 & 96.34 & 95.12 & 92.97 & **90.02** \\ FCBF & 75.21 & 98.63 & 96.51 & 92.68 & **90.75** \\ FCBF–PSO & 78.41 & 98.73 & 98.98 & 95.28 & **92.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of each feature-selection method (\(\infty\) dB). Second, as shown in Table 7, when the white noise increases to SNR = 30 dB, the method of combining HHT with SU value can maintain the accuracy of 81.08% when the number of features decreases.