Classifier Comparison for Failure Detection of Induction Motors Using Current Signal

Gyubeom Han, Jong-Kook Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Induction motor is widely used in the industry area and the bearing is one of the key mechanical components. The bearing minimizes the friction between the rotating part and stationary part of the rotating machine. It is important to monitor the bearing condition to give a warning before serious failures occur. The fault detection through electrical monitoring has been studied for the last several decades. Although they detect warning signs before serious problems occur, it does not always work when the sampling time is short. This research proposes a learning model for induction motor to diagnose bearing failures which learns features from electrical signatures. This experimental study uses data obtained from 415V, 55KW induction motor and clearance modified plain bearings.

Original languageEnglish
Title of host publicationICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages28-31
Number of pages4
Volume2018-July
ISBN (Print)9781538646465
DOIs
Publication statusPublished - 2018 Aug 14
Event10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic
Duration: 2018 Jul 32018 Jul 6

Other

Other10th International Conference on Ubiquitous and Future Networks, ICUFN 2018
CountryCzech Republic
CityPrague
Period18/7/318/7/6

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Keywords

  • classifier
  • fault diagonosis
  • plain bearing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

Cite this

Han, G., & Kim, J-K. (2018). Classifier Comparison for Failure Detection of Induction Motors Using Current Signal. In ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks (Vol. 2018-July, pp. 28-31). [8436977] IEEE Computer Society. https://doi.org/10.1109/ICUFN.2018.8436977