A systematic literature review of deep learning for vibration-based fault diagnosis of critical rotating machinery: Limitations and challenges

Omri Matania, Itai Dattner, Jacob Bortman, Ron S. Kenett, Yisrael Parmet

Research output: Contribution to journalReview articlepeer-review

Abstract

Over the last decade, thousands of papers on machine-learning for diagnosing faults in rotating machinery through vibration signals have been published. Specifically, deep learning, coupled with domain adaptation, has been replacing traditional physical and signal-processing techniques. This study systematically reviews the literature on deep learning for fault diagnosis in rotating machinery, focusing on real-world cases. The review points out current limitations in systems with several examples of labeled or unlabeled faulty signals. The study concludes by suggesting directions in which deep learning can be successfully implemented, contributing to the enhancement of current diagnostic capabilities.

Original languageEnglish
Article number118562
JournalJournal of Sound and Vibration
Volume590
StatePublished - 10 Nov 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd

Keywords

  • Deep learning
  • Fault diagnosis
  • Systematic literature review
  • Transfer across conditions
  • Transfer across machines

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Acoustics and Ultrasonics
  • Mechanical Engineering

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