# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 5 Conclusions  
This paper presents a bearing fault diagnosis method based on the feature selection method called FF-FC-MIC by exploiting the capability of MIC to capture nonlinear relevance. The results can be summarized as follows: First, the most intuitive indicator, diagnosis accuracy, shows that the proposed method can reach 97.50%, and 98.75% in terms of average diagnosis accuracy in the CWRU dataset, and reach 91.75%, 94.69%, and 99.07% in terms of average diagnosis accuracy in the CUT-2 dataset. All the accuracies are the highest compared with those in the other feature selection methods. Second, on the basis of relevance among features and relevance between features and categories, the proposed method can select relatively low-dimension feature subsets to approach a higher diagnosis accuracy, compared with the other feature selection methods. Third, by calculating \(p\)-values under the \(t\)-test, the proposed method has a significant performance improvement, compared with traditional feature selection methods. The reasons can be summarized as follows: First, since the proposed method utilizes the MIC to measure the nonlinear and non-functional relationships between features and features, and between features and categories, it can eliminate redundant and irrelevant features. Second, the proposed method employs the intersection operation to merge two subsets, avoiding subset searching. Third, extensive experiments on the CWRU dataset and CUT-2 dataset were conducted to validate the effectiveness and adaptability of the proposed method. It turns out that the proposed method performs better in both reducing feature dimensions and achieving higher diagnosis accuracy, compared with the other 3 feature selection methods.  
The relationship between the weight of features and feature subset is as essential as the work of feature selection. Besides, feature fusion technology should also be taken into consideration for feature selection. Our next work would investigate the relationship between the weight of features and feature subset and feature fusion technology to present more effective feature selection methods to reach the higher diagnosis accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods Compared** & \(p\)**-Value** &
\begin{tabular}{c} **Whether the Mean Difference between** \\ **the Two Methods Is Significant (Y/N)** \\ \end{tabular} \\ \hline Var\_FS, FF\_FC\_MIC & \(1.166\times 10^{-3}\) & Y \\ RFF\_FS, FF\_FC\_MIC & \(2.509\times 10^{-5}\) & Y \\ GBDT\_FS, FF\_FC\_MIC & \(3.576\times 10^{-2}\) & Y \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between traditional feature selection methods and the proposed method.  
**Author Contributions:** Conceptualization, X.T. and J.W.; methodology, J.W. and X.T.; software, J.W.; validation, X.T. and J.W.; investigation, X.T. and J.W.; data curation, J.W.; writing of original draft preparation, X.T. and J.W.; writing of review and editing, X.T., J.W., J.L., G.L. and J.C.; visualization, J.W.; supervision, X.T., J.L., G.L., and J.C.  
**Funding:** This research was funded by Science and Technology Major Project of Guizhou Province ([2013]6019) and Project of Guizhou High-Level Study Abroad Talents Innovation and Entrepreneurship (2018.0002), Open Fund of Guizhou Provincial Public Big Data Key Laboratory (2017 BDKFJ)019), and Guizhou University Foundation for the introduction of talent ((2016) No. 13).  
**Acknowledgments:** We gratefully acknowledge the support of [PERSON] with the guiding for this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.