Fault severity detection of a worm gearbox based on several feature extraction methods through a developed condition monitoring system

Creative Commons License

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

SN APPLIED SCIENCES, cilt.3, sa.1, 2021 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 3 Sayı: 1
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s42452-020-04131-w
  • Dergi Adı: SN APPLIED SCIENCES
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: Condition monitoring, Fault severity detection, Poincare plot, Parallel coordinates plot, Worm gearbox
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This study presents the severity detection of pitting faults on worm gearbox through the assessment of fault features extracted from the gearbox vibration data. Fault severity assessment on worm gearbox is conducted by the developed condition monitoring instrument with observing not only traditional but also multidisciplinary features. It is well known that the sliding motion between the worm gear and wheel gear causes difficulties about fault detection on worm gearboxes. Therefore, continuous monitoring and observation of different types of fault features are very important, especially for worm gearboxes. Therefore, in this study, time-domain statistics, the features of evaluated vibration analysis method and Poincare plot are examined for fault severity detection on worm gearbox. The most reliable features for fault detection on worm gearbox are determined via the parallel coordinate plot. The abnormality detection during worm gearbox operation with the developed system is performed successfully by means of a decision tree.