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A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines

Author

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  • Mathew Habyarimana

    (Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, Durban 4001, South Africa
    These authors contributed equally to this work.)

  • Abayomi A. Adebiyi

    (Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, Durban 4001, South Africa
    These authors contributed equally to this work.)

Abstract

The operational efficiency of many industrial processes is greatly affected by condition monitoring, which has become more and more important in the detection and forecast of electrical machine failures. Early identification of possible problems and prompt and precise diagnosis reduce unscheduled downtime, lower maintenance costs, and prevent catastrophic failures. Traditional human-dependent diagnostic techniques are changing as a result of advances in artificial intelligence (AI), opening the door to automated and predictive maintenance plans. This paper provides a detailed examination of artificial intelligence (AI) applications in the prediction of electrical device failures, with a focus on techniques such as fuzzy systems, expert systems, artificial neural networks (ANNs), and complex machine-learning algorithms. These methods use both historical and present data to identify and predict problems and allow timely actions. The study looks at implementation challenges for AI-based diagnostic systems, including data dependencies, processing demands, and model interpretability, in addition to highlighting recent advances such as digital twins, explainable AI, and IoT integration. This review highlights the revolutionary potential of artificial intelligence (AI) in improving the sustainability, efficiency, and dependability of electrical machine systems, especially in the context of rotating machines, by addressing existing constraints and suggesting future research routes.

Suggested Citation

  • Mathew Habyarimana & Abayomi A. Adebiyi, 2025. "A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines," Energies, MDPI, vol. 18(7), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1616-:d:1619091
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    References listed on IDEAS

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    1. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez, 2017. "State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors," Energies, MDPI, vol. 10(7), pages 1-34, July.
    2. Issouf Fofana & Yazid Hadjadj, 2016. "Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers," Energies, MDPI, vol. 9(9), pages 1-26, August.
    3. Ciobanu Dumitru & Vasilescu Maria, 2013. "Advantages and Disadvantages of Using Neural Networks for Predictions," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 444-449, May.
    4. Mahmoud Kiasari & Mahdi Ghaffari & Hamed H. Aly, 2024. "A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems," Energies, MDPI, vol. 17(16), pages 1-38, August.
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    Cited by:

    1. Nikolay Korolev, 2025. "Analytical Diagnostic and Control System of Energy and Mechanical Efficiency of Electric Drives," Energies, MDPI, vol. 18(9), pages 1-18, April.

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