Bearing fault diagnosis using deep learning techniques coupled with handcrafted feature extraction: A comparative study

Mohammed Alabsi, Yabin Liao, Ala Addin Nabulsi

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Deep learning has seen tremendous growth over the past decade. It has set new performance limits for a wide range of applications, including computer vision, speech recognition, and machinery health monitoring. With the abundance of instrumentation data and the availability of high computational power, deep learning continues to prove itself as an efficient tool for the extraction of micropatterns from machinery big data repositories. This study presents a comparative study for feature extraction capabilities using stacked autoencoders considering the use of expert domain knowledge. Case Western Reserve University bearing dataset was used for the study, and a classifier was trained and tested to extract and visualize features from 12 different failure classes. Based on the raw data preprocessing, four different deep neural network structures were studied. Results indicated that integrating domain knowledge with deep learning techniques improved feature extraction capabilities and reduced the deep neural networks size and computational requirements without the need for exhaustive deep neural networks architecture tuning and modification.

Original languageEnglish (US)
Pages (from-to)404-414
Number of pages11
JournalJVC/Journal of Vibration and Control
Volume27
Issue number3-4
DOIs
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Materials Science(all)
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering

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