Vibration-based early crack diagnosis with machine learning for spur gears


KARPAT F., DİRİK A. E., Kalay O. C., Doan O., KORCUKLU B.

ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020, Virtual, Online, 16 - 19 Kasım 2020, cilt.7B-2020 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 7B-2020
  • Doi Numarası: 10.1115/imece2020-24006
  • Basıldığı Şehir: Virtual, Online
  • Anahtar Kelimeler: Deep Learning, Fault Diagnosis, Gears, Machine Learning
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

© 2020 American Society of Mechanical Engineers (ASME). All rights reserved.Gear mechanisms are one of the most significant components of the power transmission systems. Due to increasing emphasis on the high-speed, longer working life, high torques, etc. cracks may be observed on the gear surface. Recently, Machine Learning (ML) algorithms have started to be used frequently in fault diagnosis with developing technology. The aim of this study is to determine the gear root crack and its degree with vibration-based diagnostics approach using ML algorithms. To perform early crack detection, the single tooth stiffness and the mesh stiffness calculated via ANSYS for both healthy and faulty (25-50-75-100%) teeth. The calculated data transferred to the 6-DOF dynamic model of a one-stage gearbox, and vibration responses was collected. The data gathered for healthy and faulty cases were evaluated for the feature extraction with five statistical indicators. Besides, white Gaussian noise was added to the data obtained from the 6-DOF model, and it was aimed at early fault diagnosis and condition monitoring with ML algorithms. In this study, the gear root crack and its degree analyzed for both healthy and four different crack sizes (25%-50%-75%- 100%) for the gear crack detection. Thereby, a method was presented for early fault diagnosis without the need for a big experimental dataset. The proposed vibration-based approach can eliminate the high test rig construction costs and can potentially be used for the evaluation of different working conditions and gear design parameters. Therefore, catastrophic failures can be prevented, and maintenance costs can be optimized by early crack detection.