A Machine Learning Framework for Bearing Fault Detection in Three-Phase Induction Motors

Wesam Rohouma, Ayham Zaitouny, Md Ferdous Wahid, Hassan Ali, Shady S. Refaat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Three-phase induction motors are widely employed in industry due to their rugged performance and easy maintenance. Bearing faults in three phase induction motors are responsible for 40%-50% of unplanned shutdowns in industrial settings. Therefore, early detection of bearing faults is essential to implement preventive measures and enhance planning of maintenance strategies. This paper thus proposes a machine learning (ML) framework that consistently monitors acceleration and temperature of bearing to detect bearing faults. The results show that the ML framework using k-nearest neighbor (k-NN) and support vector machine (SVM) approaches is better than the variation-based thresholding approach, where the former method is able to detect faulty conditions with more than 99% accuracy.

Original languageEnglish
Title of host publication4th International Conference on Smart Grid and Renewable Energy, SGRE 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350306262
DOIs
Publication statusPublished - 2024
Event4th International Conference on Smart Grid and Renewable Energy, SGRE 2024 - Doha, Qatar
Duration: Jan 8 2024Jan 10 2024

Publication series

Name4th International Conference on Smart Grid and Renewable Energy, SGRE 2024 - Proceedings

Conference

Conference4th International Conference on Smart Grid and Renewable Energy, SGRE 2024
Country/TerritoryQatar
CityDoha
Period1/8/241/10/24

Keywords

  • Bearing fault
  • condition monitoring
  • electric motors
  • fault detection
  • machine learning

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Modelling and Simulation
  • Artificial Intelligence
  • Energy Engineering and Power Technology

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