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Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects

Author

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  • Georgios Falekas

    (Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece)

  • Athanasios Karlis

    (Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece)

Abstract

State-of-the-art Predictive Maintenance (PM) of Electrical Machines (EMs) focuses on employing Artificial Intelligence (AI) methods with well-established measurement and processing techniques while exploring new combinations, to further establish itself a profitable venture in industry. The latest trend in industrial manufacturing and monitoring is the Digital Twin (DT) which is just now being defined and explored, showing promising results in facilitating the realization of the Industry 4.0 concept. While PM efforts closely resemble suggested DT methodologies and would greatly benefit from improved data handling and availability, a lack of combination regarding the two concepts is detected in literature. In addition, the next-generation-Digital-Twin (nexDT) definition is yet ambiguous. Existing DT reviews discuss broader definitions and include citations often irrelevant to PM. This work aims to redefine the nexDT concept by reviewing latest descriptions in broader literature while establishing a specialized denotation for EM manufacturing, PM, and control, encapsulating most of the relevant work in the process, and providing a new definition specifically catered to PM, serving as a foundation for future endeavors. A brief review of both DT research and PM state-of-the-art spanning the last five years is presented, followed by the conjunction of core concepts into a definitive description. Finally, surmised benefits and future work prospects are reported, especially focused on enabling PM state-of-the-art in AI techniques.

Suggested Citation

  • Georgios Falekas & Athanasios Karlis, 2021. "Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects," Energies, MDPI, vol. 14(18), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5933-:d:638457
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    References listed on IDEAS

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    1. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
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    7. Stéfano Frizzo Stefenon & Roberto Zanetti Freire & Leandro dos Santos Coelho & Luiz Henrique Meyer & Rafael Bartnik Grebogi & William Gouvêa Buratto & Ademir Nied, 2020. "Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System," Energies, MDPI, vol. 13(2), pages 1-19, January.
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    Cited by:

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    2. do Amaral, J.V.S. & dos Santos, C.H. & Montevechi, J.A.B. & de Queiroz, A.R., 2023. "Energy Digital Twin applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    3. Ferdaus, Md Meftahul & Dam, Tanmoy & Anavatti, Sreenatha & Das, Sarobi, 2024. "Digital technologies for a net-zero energy future: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    4. Mohamed Amine Frikha & Julien Croonen & Kritika Deepak & Yassine Benômar & Mohamed El Baghdadi & Omar Hegazy, 2023. "Multiphase Motors and Drive Systems for Electric Vehicle Powertrains: State of the Art Analysis and Future Trends," Energies, MDPI, vol. 16(2), pages 1-45, January.
    5. Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.

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