Pitting detection in a worm gearbox using artificial neural networks


ÜMÜTLÜ R. C., Hizarci B., ÖZTÜRK H., KIRAL Z.

45th International Congress and Exposition on Noise Control Engineering: Towards a Quieter Future, INTER-NOISE 2016, Hamburg, Germany, 21 - 24 August 2016, pp.6526-6534, (Full Text)

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Hamburg
  • Country: Germany
  • Page Numbers: pp.6526-6534
  • Keywords: Artificial neural networks, Vibration, Worm gear
  • Dokuz Eylül University Affiliated: Yes

Abstract

© 2016, German Acoustical Society (DEGA). All rights reserved.Diagnosis of worm gear faults using vibration analysis is difficult, for this reason; there have been quite little publications, although worm gears are used significant machines in assorted industrial fields. Whenever a defect occurs in a worm system (e.g. pitting, abrasive wear), the performances of the gears deteriorate. Therefore, transmission of motion and power cannot be transferred as demanded. As a result, occurrence of fatal defects becomes inevitable. This paper focuses upon the early detection of localized pitting damages in a worm gearbox using artificial neural networks (ANN) and vibration analysis. Worm gear vibrations are acquired from an experimental rig utilizing a 1/15 worm gearbox. Statistical parameters of vibration signals in the frequency domains are used as an input to classifier ANN for multi-class recognition.