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Real-Time Digital Twins for Intelligent Fault Diagnosis and Condition-Based Monitoring of Electrical Machines

Author

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  • Shahin Hedayati Kia

    (MIS Laboratory UR4290, University of Picardie “Jules Verne”, 33 rue St Leu, 80039 Amiens, France
    These authors contributed equally to this work.)

  • Larisa Dunai

    (Departamento de Ingeniería Gráfica, Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
    These authors contributed equally to this work.)

  • José Alfonso Antonino-Daviu

    (Instituto de Tecnología Eléctrica, Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
    These authors contributed equally to this work.)

  • Hubert Razik

    (Laboratory Ampère UMR5005, University of Lyon 1, 69622 Villeurbanne, France
    These authors contributed equally to this work.)

Abstract

This article presents an overview of selected research focusing on digital real-time simulation (DRTS) in the context of digital twin (DT) realization with the primary aim of enabling the intelligent fault diagnosis (FD) and condition-based monitoring (CBM) of electrical machines. The concept of standalone DTs in conventional multiphysics digital offline simulations (DoSs) is widely utilized during the conceptualization and development phases of electrical machine manufacturing and processing, particularly for virtual testing under both standard and extreme operating conditions, as well as for aging assessments and lifecycle analysis. Recent advancements in data communication and information technologies, including virtual reality, cloud computing, parallel processing, machine learning, big data, and the Internet of Things (IoT), have facilitated the creation of real-time DTs based on physics-based (PHYB), circuit-oriented lumped-parameter (COLP), and data-driven approaches, as well as physics-informed machine learning (PIML), which is a combination of these models. These models are distinguished by their ability to enable real-time bidirectional data exchange with physical electrical machines. This article proposes a predictive-level framework with a particular emphasis on real-time multiphysics modeling to enhance the efficiency of the FD and CBM of electrical machines, which play a crucial role in various industrial applications.

Suggested Citation

  • Shahin Hedayati Kia & Larisa Dunai & José Alfonso Antonino-Daviu & Hubert Razik, 2025. "Real-Time Digital Twins for Intelligent Fault Diagnosis and Condition-Based Monitoring of Electrical Machines," Energies, MDPI, vol. 18(17), pages 1-29, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4637-:d:1738936
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    References listed on IDEAS

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