Neural network application for fault detection in electric motors

Şeker Ş. S., Kayran A. H.

19th Australasian Universities Power Engineering Conference: Sustainable Energy Technologies and Systems, AUPEC'09, Adelaide, Australia, 27 - 30 September 2009 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Adelaide
  • Country: Australia
  • Keywords: Ageing process, Bearing damage, Fault detection, Indiction motor, Neural network
  • Istanbul Technical University Affiliated: Yes

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.