Bearing fault diagnosis employing Gabor and augmented architecture of convolutional neural network

  • N. Fathiah Waziralilah Intelligent Dynamics System, Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia, Phone: +60322031351
  • Aminudin Abu Intelligent Dynamics System, Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia, Phone: +60322031351
  • M. H. Lim Institute of Noise and Vibration, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
  • Lee Kee Quen Intelligent Dynamics System, Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia, Phone: +60322031351
  • Ahmed Elfakarany Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
Keywords: Bearing fault diagnosis, convolutional neural network, deep learning, Gabor spectrogram, image processing

Abstract

The vast impact on machinery that is rooted by bearing degradation thus pinpointing bearing fault diagnosis as indubitably very crucial. The research is innovated to diagnose the fault in bearing by implementing deep learning approach which is Convolutional Neural Network (CNN) that has superiority over image processing and pattern recognition. A novel model comprises of Gabor Transform and CNN is proposed whereby Gabor Transform is utilized in representing the raw vibration signals into its image representation. The CNN architecture is augmented for a better accuracy of the bearing fault diagnosis model. To date, the method combination has never been deployed in establishing fault diagnosis model. Plus, the usage of Gabor Transform in mechanical area especially in bearing fault diagnosis is meagrely reported. Scant researches in mechanical diagnosis are dedicated to work on the image representation of the vibration data whereas the CNN works better when fed by images input due to its unique strength of CNN in images processing and spatial awareness. At the end of the research, it is perceived that the proposed model comprises of Gabor Transform and CNN can diagnose the bearing faults with 100% accuracy and perform better than when CNN is fed with raw signals.

Published
2019-09-27
How to Cite
Waziralilah, N. F., Abu, A., Lim, M. H., Quen, L., & Elfakarany, A. (2019). Bearing fault diagnosis employing Gabor and augmented architecture of convolutional neural network. Journal of Mechanical Engineering and Sciences, 13(3), 5689 - 5702. https://doi.org/10.15282/jmes.13.3.2019.29.0455