Title

Dimensionality Reduction-Based Diagnosis of Bearing Defects in Induction Motors

Document Type

Conference Proceeding

Publication Date

11-27-2017

Publication Title

2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017

Volume

2017-January

First Page

2539

Last Page

2544

Abstract

Efficient diagnosis of bearing defects in induction motors usually requires extracting informative features from the vibration signal and efficiently reducing the dimensionality of the features. In this paper, the vibration signal is primarily analyzed by the empirical mode decomposition technique to extract informative intrinsic mode functions as a set of features. The dimensionality of the extracted feature set is reduced by means of maximally collapsing metric learning (MCML) to create an informative set of small-sized features for fault classification. MCML is an efficient supervised dimensionality reduction technique which aims to collapse patterns of the similar class to a point in the feature space while separates patterns of other classes to the maximum extent possible. To compare the performance of MCML, other state-of-the-art unsupervised and supervised techniques are used for the dimension reduction of the features. The fault diagnosis unit includes various classifiers which aim to diagnose multiple bearing defects that are ball, inner race and outer race defects of different diameters.

DOI

10.1109/SMC.2017.8123006

ISBN

9781538616451

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