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A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Author

Listed:
  • Zijian Liu

    (Key Laboratory of Vehicle Transmission, China North Vehicle Research Institute, Beijing 100072, China)

  • Pinjia Zhang

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Shan He

    (Department of Energy Technology, Aalborg University, DK-9220 Aalborg East, Denmark)

  • Jin Huang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research.

Suggested Citation

  • Zijian Liu & Pinjia Zhang & Shan He & Jin Huang, 2021. "A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines," Energies, MDPI, vol. 14(14), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4296-:d:595556
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    References listed on IDEAS

    as
    1. Faiz, Jawad & Moosavi, S.M.M., 2016. "Eccentricity fault detection – From induction machines to DFIG—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 169-179.
    2. Anmol Aggarwal & Elias G. Strangas, 2019. "Review of Detection Methods of Static Eccentricity for Interior Permanent Magnet Synchronous Machine," Energies, MDPI, vol. 12(21), pages 1-20, October.
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    Cited by:

    1. Muhammad Usman Sardar & Toomas Vaimann & Lauri Kütt & Ants Kallaste & Bilal Asad & Siddique Akbar & Karolina Kudelina, 2023. "Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques," Energies, MDPI, vol. 16(15), pages 1-41, July.
    2. 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.

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