A multi-label classification approach for the detection of broken bars and mixed eccentricity faults using the start-up transient

George Georgoulas, Vicente Climente-Alarcon, Jose A. Antonino-Daviu, Chrysostomos D. Stylios, Antero Arkkio, George Nikolakopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault, using the power-set approach. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity. For the feature extraction stage, the time-frequency representation, resulting from the application of the short time Fourier transform of the start-up current is exploited. The proposed approach is validated using simulation data with promising results.

Original languageEnglish
Title of host publicationProceedings of the 14th IEEE International Conference on Industrial Informatics, INDIN 2016
PublisherIEEE
Pages430-433
Number of pages4
ISBN (Electronic)9781509028702
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Industrial Informatics - Poitiers, France
Duration: 19 Jul 201621 Jul 2016
Conference number: 14
https://ieee-indin2016.sciencesconf.org/

Publication series

NameIEEE International Conference on Industrial Informatics
PublisherIEEE
ISSN (Print)1935-4576
ISSN (Electronic)2378-363X

Conference

ConferenceIEEE International Conference on Industrial Informatics
Abbreviated titleINDIN
CountryFrance
CityPoitiers
Period19/07/201621/07/2016
Internet address

Keywords

  • mixed eccentricity
  • multi-label classification
  • piecewise aggregate approximation
  • rotor broken bars

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