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Review of Fault Detection and Diagnosis Techniques for AC Motor Drives

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Listed:
  • Muhammed Ali Gultekin

    (Electrical and Computer Engineering Department, University of Connecticut, Storrs, CT 06269, USA)

  • Ali Bazzi

    (Electrical and Computer Engineering Department, University of Connecticut, Storrs, CT 06269, USA)

Abstract

Condition monitoring in electric motor drives is essential for operation continuity. This article provides a review of fault detection and diagnosis (FDD) methods for electric motor drives. It first covers various types of faults, their mechanisms, and approaches to detect and diagnose them. The article categorizes faults into machine faults, power electronics (PE) faults, DC link capacitor faults, and sensor faults, and discusses FDD methods. FDD methods for machines are categorized as statistical methods, machine-learning methods, and deep-learning methods. PE FDD methods are divided into logic-based, residual-based, and controller-aided methods. DC link capacitor and sensor faults are briefly explained. Machine and PE faults are listed and presented as tables for easy comparison and fast referencing. Most papers are selected from the past five years but older references are added when necessary. Finally, a discussion section is added to reflect on current trends and possible future research areas.

Suggested Citation

  • Muhammed Ali Gultekin & Ali Bazzi, 2023. "Review of Fault Detection and Diagnosis Techniques for AC Motor Drives," Energies, MDPI, vol. 16(15), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5602-:d:1202158
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    References listed on IDEAS

    as
    1. David Gonzalez-Jimenez & Jon del-Olmo & Javier Poza & Fernando Garramiola & Izaskun Sarasola, 2021. "Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines," Energies, MDPI, vol. 14(16), pages 1-21, August.
    2. Andre S. Barcelos & Antonio J. Marques Cardoso, 2021. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms," Energies, MDPI, vol. 14(9), pages 1-14, April.
    3. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
    4. Jing Tang & Yongheng Yang & Jie Chen & Ruichang Qiu & Zhigang Liu, 2019. "Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection," Energies, MDPI, vol. 13(1), pages 1-17, December.
    5. Jing Tang & Jie Chen & Kan Dong & Yongheng Yang & Haichen Lv & Zhigang Liu, 2019. "Modeling and Evaluation of Stator and Rotor Faults for Induction Motors," Energies, MDPI, vol. 13(1), pages 1-20, December.
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    Citations

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    Cited by:

    1. Marco Pastura & Mauro Zigliotto, 2024. "Fault Diagnosis in Electrical Machines for Traction Applications: Current Trends and Challenges," Energies, MDPI, vol. 17(21), pages 1-20, October.
    2. Natalia Koteleva & Nikolai Korolev, 2024. "A Diagnostic Curve for Online Fault Detection in AC Drives," Energies, MDPI, vol. 17(5), pages 1-14, March.
    3. Przemyslaw Pietrzak & Piotr Pietrzak & Marcin Wolkiewicz, 2024. "Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors," Energies, MDPI, vol. 17(2), pages 1-22, January.

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