# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 4 Experimental Results and Analysis
### Experiments on CUT-2 Datasets
#### 4.2.2 Result Analysis of CUT-2 Datasets  
According to the proposed feature selection method, the features selected on 3 datasets are shown in Table 6. From Table 6, it can be seen that 9, 9 and 11 features were selected at 3 speeds. Under most circumstances of 3 motor speeds from Table 7, the proposed feature selection method can reach the highest diagnosis accuracy, comparing to the other 3 methods. In addition, it performs a certain adaptability and stability on SVM and KNN. At the speeds of 2000 rpm, 2500 rpm, and 3000 rpm, the average diagnosis accuracies are 91.75%, 94.69% and 99.07%, respectively. From this phenomenon, it was found that the diagnosis accuracy increases while the motor speed increases. There are 2 factors to cause this phenomenon. First, our bearings are small in both size and fault size, which means the vibration signal is less obvious in a lower speed than in a higher speed, leading to the phenomenon of a lower diagnosis accuracy in low speed and a higher diagnosis accuracy in high speed. Second, a large difference in motor speed, ranging from 2000 rpm to 3000 rpm, causes such a diversity of diagnosis accuracy increases while the motor speed increases. For further analysis, compared with the other 3 methods, the proposed method can improve the accuracy by about an average of 1.32% at least, and by about an average of 6.94% at most. In term of diagnosis accuracy, the proposed method has a better performance than the other 3 feature selection methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Motor Speed (rpm)** & **All Features** & **Selected Features** & **Selected Number** \\ \hline
2000 & 5 & 0, 2, 3, 8, 10, 11, 19, 20, 22 \\ \hline
3000 & 5 & 0, 3, 8, 9, 11, 14, 15, 16, 20, 21, 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dataset features selected by the proposed method. (The pictures are the visualization results for the corresponding speed samples. The horizontal axis in the figure represents the number of features, and the vertical axis represents the first 13 samples. For the sake of beauty, the first 13 samples are chosen). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Conditions of the Bearings** & **Fault Size (mm)** & **Number of Samples** & **Class** \\ \hline Normal condition & & 200 & 0 \\ \hline Inner race fault & 0.2 & 100 & 1 \\  & 0.3 & 100 & 1 \\ \hline Outer race fault & 0.2 & 100 & 2 \\  & 0.3 & 100 & 3 \\ \hline Baller fault & 0.2 & 100 & 3 \\  & 0.3 & 100 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Datasets obtained at motor speeds of 2000 rpm, 2500 rpm and 3000 rpm on the CUT-2 platform. In terms of number of features selected by different methods, the numbers of features selected among the 4 methods were compared. All the numbers of features selected by 4 methods are shown in Figure 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Motor Speed (rpm)**} & \multirow{2}{*}{**Feature Selection Methods**} & \multicolumn{2}{c}{**Classification Models**} \\ \cline{3-4}  & & **SVM** & **KNN** \\ \hline \multirow{4}{*}{2000} & Var\_FS & 0.8688 & 0.8750 \\  & RFE\_FS & 0.8938 & 0.8875 \\  & GBDT\_FS & 0.9313 & 0.9012 \\  & FF\_FC\_MIC & 0.9225 & 0.9125 \\ \hline \multirow{4}{*}{2500} & Var\_FS & 0.8562 & 0.8250 \\  & RFE\_FS & 0.8688 & 0.8812 \\  & GBDT\_FS & 0.9187 & 0.9175 \\  & FF\_FC\_MIC & 0.9625 & 0.9313 \\ \hline \multirow{4}{*}{3000} & Var\_FS & 0.9437 & 0.9250 \\  & RFE\_FS & 0.9187 & 0.9062 \\ \cline{1-1}  & GBDT\_FS & 0.9750 & 0.9875 \\ \cline{1-1}  & FF\_FC\_MIC & 0.9938 & 0.9875 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of classification accuracy of SVM and KNN at motor speeds of 2000 rpm, 2500 rpm and 3000 rpm using different feature selection methods.