# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 4 Experimental Results and Analysis
### Experiments on CWRU Datasets
#### 4.1.2 Result Analysis of CWRU Datasets  
According to the proposed feature selection method, features selected by the proposed method are shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Conditions of the Bearings** & **Fault Size (inch)** & **Number of Samples** & **Class** \\ \hline Normal & & 150 & 0 \\ \hline \multirow{3}{*}{Inner race fault} & 0.007 & 50 & \\  & 0.014 & 50 & 1 \\  & 0.021 & 50 & \\ \hline \multirow{3}{*}{Outer race fault} & 0.007 & 50 & \\  & 0.014 & 50 & 2 \\  & 0.021 & 50 & \\ \hline \multirow{3}{*}{Baller fault} & 0.007 & 50 & \\  & 0.014 & 50 & 3 \\ \cline{1-1}  & 0.021 & 50 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Datasets obtained at the motor speeds of 1750 rpm and 1772 rpm on the CWRU bearing experimental platform. Figure 2: CWRU bearing experimental platform. As is shown in Table 3, at the motor speed of 1750 rpm, the proposed method selected 7 features from 25 features in total, and selected 9 features at the speed of 1772 rpm. KNN and SVM classifiers and 3 traditional feature selection methods were applied to validate the proposed method. As mentioned before, 2 datasets were constructed at 1750 rpm and 1772 rpm. Both of them were used in the training and testing processes. The results are shown in Table 4.  
As shown in Table 4, the proposed method has a higher accuracy than the other 3 feature selection methods. Especially, the best classification accuracy has achieved 100% at the motor speed of 1772 rpm with KNN. Comparing with the other 3 methods, the proposed method can improve the accuracy by about an average of 1.21% at least, and by about an average of 15.25% at most. Meanwhile, the classification accuracies of the proposed method at different motor speeds with 2 classifiers reached are all above 95%. Above all, it is obvious that the proposed method has a pretty performance on the aspect of diagnosis accuracy. In addition, in order to validate the propose method's effectiveness, the number of features selected is compared between the proposed method and the other 3 traditional methods. All the numbers of features selected by these 4 methods are shown in Figure 3. \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}{*}{1750} & Var\_FS & 0.8750 & 0.8750 \\  & RFE\_FS & 0.8500 & 0.8333 \\ \cline{1-1}  & GBDT\_FS & 0.9417 & 0.9600 \\ \cline{1-1}  & FF\_FC\_MIC & 0.9583 & 0.9750 \\ \hline \multirow{4}{*}{1772} & Var\_FS & 0.7750 & 0.7417 \\ \cline{1-1}  & RFE\_FS & 0.8000 & 0.7666 \\ \cline{1-1} \cline{2-4}  & GBDT\_FS & 0.9833 & 0.9917 \\ \cline{1-1}  & FF\_FC\_MIC & 0.9917 & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of classification accuracy of SVM and KNN at motor speeds of 1750 rpm and 1772 rpm using different feature selection methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Motor Speed (rpm)** & **All Features** & **Selected Features** & **Selected Number** \\ \hline \hline
1772 & 1750 & 1750 & 1750 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CWRU 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). In Figure 3, the number of features selected by the proposed method under both motor speeds is the minimum, comparing to those in the other 3 methods. In other words, the proposed method achieves a higher diagnosis accuracy based on fewer features. Fewer features indicate lower data dimension and lower computation complexity for classifiers, which means the proposed method can reach a better performance with respect to feature dimension.