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Artificial intelligence (AI) is reshaping maintenance strategies in energy systems by enabling predictive approaches over traditional reactive or time-based methods. These conventional techniques often result in service interruptions, increased operational costs, and safety concerns. This study reviews the application of AI-powered predictive maintenance (PdM) to enhance system reliability and efficiency. By leveraging real-time data, historical trends, and advanced models, such as machine learning, deep learning, and edge computing, AI systems can anticipate equipment failures, reduce unplanned downtime, and optimise resource allocation. Core technologies discussed include Internet of Things (IoT) integration, digital twins, and explainable AI (XAI), which contribute to improved decision-making and system transparency. This review provides a structured analysis of the role of AI in energy system maintenance, highlights implementation challenges, and offers a conceptual framework and taxonomy to guide future research.
Azodo et al. (Tue,) studied this question.