Statements on wavelet packet energy–entropy signatures and filter influence in fault diagnosis of induction motor in non-stationary operations

Abstract

Electric motors are components of great importance in mechanical systems and in the majority of the equipment used in industrial plants. The several faults that occur in the induction machines may induce severe consequences in the industrial process. Many of these faults are progressive. In this work, a contribution to the study of signal-processing techniques based on wavelet packet transform for parameter extraction of energy and entropy from vibration signals for the detection of faults in the non-stationary operation (start of the motor) is presented. Together with the wavelet transform, methods of dimensionality reduction such as principal component analysis, linear discriminant analysis, and independent components analysis are used. In addition, the use of an experimental bench shows that the model of extraction and classification proposed present high precision for fault classification.