Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection

Applied Sciences

Jiachen Wang¹  Jianguang Lu¹  Jiadui Chen¹  Xianghong Tang¹  Guokai Liu² 

[1] Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China;State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

关键词: feature selection;    Maximum Information Coefficient (MIC);    FF-MIC;    FC-MIC;    bearing fault diagnosis;   

DOI  :  10.3390/app8112143

来源: DOAJ

【 摘 要 】

Effective feature selection can help improve the classification performance in bearing fault diagnosis. This paper proposes a novel feature selection method based on bearing fault diagnosis called Feature-to-Feature and Feature-to-Category- Maximum Information Coefficient (FF-FC-MIC), which considers the relevance among features and relevance between features and fault categories by exploiting the nonlinearity capturing capability of maximum information coefficient. In this method, a weak correlation feature subset obtained by a Feature-to-Feature-Maximum Information Coefficient (FF-MIC) matrix and a strong correlation feature subset obtained by a Feature-to-Category-Maximum Information Coefficient (FC-MIC) matrix are merged into a final diagnostic feature set by an intersection operation. To evaluate the proposed FF-FC-MIC method, vibration data collected from two bearing fault experiment platforms (CWRU dataset and CUT-2 dataset) were employed. Experimental results showed that accuracy of FF-FC-MIC can achieve 97.50%, and 98.75% on the CWRU dataset at the motor speeds of 1750 rpm, and 1772 rpm, respectively, and reach 91.75%, 94.69%, and 99.07% on CUT-2 dataset at the motor speeds of 2000 rpm, 2500 rpm, 3000 rpm, respectively. A significant improvement of FF-FC-MIC has been confirmed, since the p-values between FF-FC-MIC and the other methods are 1.166 × ${10}^{-3}$ , 2.509 × ${10}^{-5}$ , and 3.576 × ${10}^{-2}$ , respectively. Through comparison with other methods, FF-FC-MIC not only exceeds each of the baseline feature selection method in diagnosis accuracy, but also reduces the number of features.

【 授权许可】

Unknown