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
Recommended Citation
Farajzadeh-Zanjani, Maryam; Razavi-Far, Roozbeh; and Saif, Mehrdad. (2017). Dimensionality Reduction-Based Diagnosis of Bearing Defects in Induction Motors. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 2017-January, 2539-2544.
https://scholar.uwindsor.ca/electricalengpub/146