Title

Efficient feature extraction of vibration signals for diagnosing bearing defects in induction motors

Document Type

Conference Proceeding

Publication Date

10-31-2016

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Volume

2016-October

First Page

4504

Last Page

4511

Abstract

This paper presents a model to extract and select a proper set of features for diagnosing bearing defects in induction motors. An efficient pre-processing of the vibration signals is of paramount importance to provide informative features for the fault classification module. The vibration signals are firstly analyzed by the wavelet packet transform to extract informative frequency domain features. The dimension of the set of extracted features is reduced by resorting to linear discriminant analysis to provide a small-size set of informative features for decision making. The fault classification module contains different classifiers that can learn the features-faults relations and classify multiple bearing defects including ball, inner race and outer race defects of different diameters. Experimental results verify the effectiveness of the proposed technique for diagnosing multiple bearing defects in induction motors.

DOI

10.1109/IJCNN.2016.7727789

ISBN

9781509006199

Link to Full Text

Share

COinS