Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection

• Main Authors: Xianghong Tang, Jiachen Wang, Jianguang Lu, Guokai Liu, Jiadui Chen
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Applied Sciences
• Subjects: feature selection; Maximum Information Coefficient (MIC); FF-MIC; FC-MIC; bearing fault diagnosis
• Online Access: https://www.mdpi.com/2076-3417/8/11/2143

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 <i>p</i>-values between FF-FC-MIC and the other methods are 1.166 &#215; <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>&#8722;</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics> </math> </inline-formula>, 2.509 &#215; <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>&#8722;</mo> <mn>5</mn> </mrow> </msup> </mrow> </semantics> </math> </inline-formula>, and 3.576 &#215; <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>&#8722;</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics> </math> </inline-formula>, 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.