Maximum Class Separability-Based Discriminant Feature Selection Using a GA for Reliable Fault Diagnosis of Induction Motors

Reliable fault diagnosis in bearing elements of induction motors, with high classification performance, is of paramount importance for ensuring steady manufacturing. The performance of any fault diagnosis system largely depends on the selection of a feature vector that represents the most distinctive fault attributes. This paper proposes a maximum class separability (MCS) feature distribution analysis-based feature selection method using a genetic algorithm (GA). The MCS distribution analysis model analyzes and selects an optimal feature vector, which consists of the most distinguishing features from a high dimensional feature space, for reliable multi-fault diagnosis in bearings. The high dimensional feature space is an ensemble of hybrid statistical features calculated from time domain analysis, frequency domain analysis, and envelope spectrum analysis of the acoustic emission (AE) signal. The proposed maximum class separability-based objective function using the GA is used to select the optimal feature set. Finally, k-nearest neighbor (k-NN) algorithm is used to validate our proposed approach in terms of the classification performance. The experimental results validate the superior performance of our proposed model for different datasets under different motor rotational speeds as compared to conventional models that utilize (1) the original feature vector and (2) a state-of-the-art average distance-based feature selection method.