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
Recommended Citation
Farajzadeh-Zanjani, Maryam; Razavi-Far, Roozbeh; Saif, Mehrdad; and Rueda, Luis. (2016). Efficient feature extraction of vibration signals for diagnosing bearing defects in induction motors. Proceedings of the International Joint Conference on Neural Networks, 2016-October, 4504-4511.
https://scholar.uwindsor.ca/electricalengpub/158