Optimized Approach of Feature Selection Based on Binary Genetic Algorithm in Classification of Induction Motor Faults

In this paper, an effective model for detection and classification of multiple faults in induction motors is presented. It used S-transform method is used to analyze current signals measured from four different motors including a healthy motor, broken rotor bars, bearing damage, stator winding short-circuits fault. The feature set is extracted based on signal spectrum. With strong exploration capabilities in the search space, binary genetic algorithm (BGA) is proposed to select the optimal feature subset. As the classifier, the backpropagation neural network and support vector machine are used. The simulation results showed that the average accuracy of 100 trails is 98.3\% and the optimal feature subset equal to 36\% of total original features. That means the number of redundant features removed is 64\%. In conclusion, the proposed model combined with BGA reached highly effective in the classification of induction motor.