Establish Induction Motor Fault Diagnosis System Based on Feature Selection Approaches with MRA

• Main Authors: Chun-Yao Lee, Meng-Syun Wen
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Processes
• Subjects: fault detection; feature selection; multiresolution analysis; correlation-based feature selection; correlation and fitness value-based feature selection; artificial neural network
• Online Access: https://www.mdpi.com/2227-9717/8/9/1055

This paper proposes a feature selection (FS) approach, namely, correlation and fitness value-based feature selection (CFFS). CFFS is an improvement feature selection approach of correlation-based feature selection (CFS) for the common failure cases of the induction motor. CFFS establishes the induction motor fault detection (FD) system with artificial neural network (ANN). This study analyzes the current signal of the induction motor with multiresolution analysis (MRA), extracts the features, and uses feature selection approaches (ReliefF, CFS, and CFFS) to reduce the number of features and maintain the accuracy of the induction motor fault detection system. Finally, the induction motor fault detection system is trained by the feature selection approaches selected features. The best induction motor fault detection system will be established through the comparison of the efficiency of these FS approaches.