Diagnosis of bearing defects in induction motors by fuzzy-neighborhood density-based clustering

Document Type

Conference Proceeding

Publication Date

3-2-2016

Publication Title

Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

First Page

935

Keywords

Bearing defects, Fault diagnosis, Fuzzy-neighborhood density-based clustering, Induction motors

Last Page

940

Abstract

In this paper, a supervised fuzzy-neighborhood density-based clustering approach is proposed for the fault diagnosis of induction motors' bearings. The proposed approach makes use of the labeled data regarding the actual classes of faulty and fault-free cases, in order to train the fuzzy-neighborhood density-based clustering algorithm in a supervised manner, by resorting to an invasive weed optimization algorithm that aims to minimize an error-based objective function. The proposed classifier can properly classify multi-class data with complex and variously shaped decision boundaries among the different classes of faults and the fault-free state, and is robust against noise. This is due mainly to the fact that the classifier is constructed using the fuzzy-neighborhood density based clustering method, which is not sensitive to the geometrical shape of clusters in the feature space.

DOI

10.1109/ICMLA.2015.114

ISBN

9781509002870

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