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

Technical Paper
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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.

Keywords

Induction motors Fault diagnosis Wavelet packet transform (WPT) Principal component analysis (PCA) Independent component analysis (ICA) Linear discriminant analysis (LDA) 

Notes

Acknowledgements

The authors want to express their thanks to CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for the financial support.

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Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.Faculty of EngineeringFederal University of Grande Dourados (UFGD), Cidade UniversitáriaDouradosBrazil
  2. 2.Faculty of Mechanical EngineeringUniversity of Campinas (UNICAMP), Cidade UniversitáriaRio de JaneiroBrazil

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