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: Contribution to conferencePaper

16 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 languageEnglish
Pages935 - 940
DOIs
Publication statusPublished - 2015
Event2015 IEEE 14th International Conference on Machine Learning and Applications - Miami, United States
Duration: 9 Dec 201511 Dec 2015

Conference

Conference2015 IEEE 14th International Conference on Machine Learning and Applications
Abbreviated titleICMLA
CountryUnited States
CityMiami
Period9/12/1511/12/15

Bibliographical note

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Keywords

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

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  • Cite this

    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. 935 - 940. Paper presented at 2015 IEEE 14th International Conference on Machine Learning and Applications, Miami, United States. https://doi.org/10.1109/ICMLA.2015.114