Genetic Algorithm Based Discriminant Feature Selection for Improved Fault Diagnosis of Induction Motor

Corresponding author. Abstract In this paper, we present an efficient model for reliable fault diagnosis of the induction motor. This is now a growing demand for high classification accuracy in fault diagnosis. However, the system performance is highly dependable on superior feature analysis. But, it’s still crucial and computational complex to select discernment features, thus, a new genetic algorithm (GA) with optimum class separability criteria is utilized to find most discriminate features from a hybrid feature vector. For this approach, wavelet packet decomposition (WPD) is applied on Acoustic Emission (AE) fault signal and hybrid statistical features are extracted from a decomposed wavelet packet coefficient, which has maximum energy. GA and Euclidean distance based novel, optimum class separability (OCS) are used to select the optimal low-dimensional feature set from high dimensional feature set. The efficacy of this proposed model, in terms of classification accuracy, is validated by the knearest neighbor (k-NN) classifier. Experimental results show that the proposed model has a superior classification, yielding an average classification accuracy above 98%.