Fault Diagnosis System of Induction Motors Using Feature Extraction, Feature Selection and Classification Algorithm

Abstract

This paper proposes a fault diagnosis system for induction motor which integrates principal component analysis (PCA), genetic algorithm (GA) and artificial neural network (ANN). Vibration signals and stator current signals are measured as the fault diagnosis media. Many sensors result in many features to ANN. In order to avoid the curse of dimensionality phenomenon and improve the classification rate, PCA and GA are employed to reduce the feature dimensionality of the measured data. PCA removes the relative features. Then the irrelative features after PCA are selected by GA to find better feature subset as inputs to the network under a few population and generations. GA is also used to optimize the ANN structure in that the selected PCs feature subset is evaluated by it. The efficiency of the proposed system is validated by comparison of other three systems: ANN only, ANN with PCA and ANN with GA. The classification success rate for the ANN with PCA and GA was 100% for validation, while the rates of ANN only, ANN with PCA and ANN with GA were 83.33%, 86.67% and 98.89%, respectively.