Induction Motor Fault Classification Based on Combined Genetic Algorithm with Symmetrical Uncertainty Method for Feature Selection Task

Abstract

This research proposes a method to improve the capability of a genetic algorithm (GA) to choose the best feature subset by incorporating symmetrical uncertainty (SU) to rank the features and remove redundant features. The proposed method is a combination of symmetrical uncertainty and a genetic algorithm (SU-GA). In this study, feature selection is implemented on four different conditions of an induction motor: normal, broken bearings, a broken rotor bar, and a stator winding short circuit. The Hilbert-Huang transform (HHT) is then used to analyze the current signal in these four motor conditions. After that, the feature selection is used to find the best feature subset for the classification task. A support vector machine (SVM) was used for the feature classification. Three feature selection methods were implemented: SU, GA, and SU-GA. The results show that SU-GA obtained better accuracy with fewer selected features. In addition, to simulate and analyze the actual operating situation of the induction motors, three different magnitudes of white noise were added with the following signal-to-noise ratios (SNR): 40 dB, 30 dB, and 20 dB. Finally, the results show that the proposed method has a better classification capability.