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

Mathew Habyarimana () and Abayomi A. Adebiyi
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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
Abayomi A. Adebiyi: Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, Durban 4001, South Africa

Energies, 2025, vol. 18, issue 7, 1-21

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.

Keywords: artificial intelligence; electrical machines; faults; predict (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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