Neural network application for fault detection in electric motors

Serhat Seker*, Ahmet H. Kayran

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

This research describes the monitoring of the fundamental spectral features of the bearing damage through accelerated aging studies for induction motors with a power rating of 5 HP. For this aim, the bearing damage is characterized between 2-4 kHz through the spectral analysis methods applied to motor vibration signals. Also, coherence analysis approach, defined between the stator currents and vibration signals, is used for as another indicator of the bearing damage. After the computation of the coherences, a neuro-detector based on the auto-associative neural structure is trained in the frequency domain. Hence, the bearing damage detection is realized by observing the changes in the errors (residuals) generated by the neural net.

Original languageEnglish
Title of host publicationAUPEC'09 - 19th Australasian Universities Power Engineering Conference
Subtitle of host publicationSustainable Energy Technologies and Systems
Publication statusPublished - 2009
Event19th Australasian Universities Power Engineering Conference: Sustainable Energy Technologies and Systems, AUPEC'09 - Adelaide, Australia
Duration: 27 Sept 200930 Sept 2009

Publication series

NameAUPEC'09 - 19th Australasian Universities Power Engineering Conference: Sustainable Energy Technologies and Systems

Conference

Conference19th Australasian Universities Power Engineering Conference: Sustainable Energy Technologies and Systems, AUPEC'09
Country/TerritoryAustralia
CityAdelaide
Period27/09/0930/09/09

Keywords

  • Ageing process
  • Bearing damage
  • Fault detection
  • Indiction motor
  • Neural network

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