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

M. Farajzadeh-Zanjani; R. Razavi-Far; M. Saif; J. Zarei; Vasile Palade

doi:10.1109/ICMLA.2015.114

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

M. Farajzadeh-Zanjani, R. Razavi-Far, M. Saif, J. Zarei, Vasile Palade

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

20 Citations (Scopus)

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.

Original language	English
Title of host publication	2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)
Publisher	IEEE
Pages	935 - 940
Number of pages	16
ISBN (Electronic)	9781509002870
DOIs	https://doi.org/10.1109/ICMLA.2015.114
Publication status	Published - 3 Mar 2015
Event	2015 IEEE 14th International Conference on Machine Learning and Applications - Miami, United States Duration: 9 Dec 2015 → 11 Dec 2015

Conference

Conference	2015 IEEE 14th International Conference on Machine Learning and Applications
Abbreviated title	ICMLA
Country/Territory	United States
City	Miami
Period	9/12/15 → 11/12/15

Bibliographical note

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

bearing defects
fault diagnosis
fuzzy-neighborhood density-based clustering
induction motors

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10.1109/ICMLA.2015.114

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Diagnosis of bearing defects in induction motors by fuzzy: neighborhood density-based clustering. / Farajzadeh-Zanjani, M.; Razavi-Far, R.; Saif, M. et al.
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. p. 935 - 940.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Farajzadeh-Zanjani, M, Razavi-Far, R, Saif, M, Zarei, J & Palade, V 2015, Diagnosis of bearing defects in induction motors by fuzzy: neighborhood density-based clustering. in 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, pp. 935 - 940, 2015 IEEE 14th International Conference on Machine Learning and Applications, Miami, United States, 9/12/15. https://doi.org/10.1109/ICMLA.2015.114

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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. ",

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AB - 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.

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

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Keywords

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