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, Almanya, 21 - 24 Ağustos 2016, ss.6526-6534 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Basıldığı Şehir: Hamburg
  • Basıldığı Ülke: Almanya
  • Sayfa Sayıları: ss.6526-6534
  • Anahtar Kelimeler: Artificial neural networks, Vibration, Worm gear
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

© 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.