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The Bearing Faults Detection Methods for Electrical Machines—The State of the Art

Author

Listed:
  • Muhammad Amir Khan

    (Department of Electrical Power Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

  • Bilal Asad

    (Department of Electrical Power Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
    Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia)

  • Karolina Kudelina

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia)

  • Toomas Vaimann

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia)

  • Ants Kallaste

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia)

Abstract

Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work.

Suggested Citation

  • Muhammad Amir Khan & Bilal Asad & Karolina Kudelina & Toomas Vaimann & Ants Kallaste, 2022. "The Bearing Faults Detection Methods for Electrical Machines—The State of the Art," Energies, MDPI, vol. 16(1), pages 1-54, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:296-:d:1016766
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    References listed on IDEAS

    as
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