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<p>The growing complexity and operational requirements of modern power plants demand advanced monitoring and maintenance strategies to maintain system re- liability, efficiency, and safety. Conventional maintenance methods, including re- active and scheduled maintenance, often result in unexpected equipment failures, higher operational expenses, and decreased system availability. In recent years, artificial intelligence (AI) has gained significant attention as an effective approach for intelligent fault detection and predictive maintenance in power generation sys- tems. This review provides a comprehensive analysis of AI-based techniques used for fault diagnosis and predictive maintenance in power plants. The paper explores a range of machine learning and deep learning methods—such as artificial neural networks, support vector machines, decision trees, random forests, and deep neural networks—that are applied to analyze operational data and identify anomalies in critical components, including turbines, boilers, generators, and transformers. Moreover, the study discusses data-driven predictive maintenance frameworks that integrate sensor data, Internet of Things (IoT) technologies, and advanced analytical techniques to predict potential failures before they occur. The advan- tages, limitations, and effectiveness of various AI-based approaches reported in re- cent studies are critically reviewed. In addition, the paper addresses several impor- tant challenges, including data quality issues, model interpretability, computational complexity, and the integration of AI systems with existing industrial infrastruc- tures. Finally, the review outlines potential future research directions aimed at im- proving the robustness and real-world implementation of AI-driven maintenance solutions in power plants. Overall, this study seeks to provide researchers and in- dustry professionals with a comprehensive understanding of recent advancements and emerging opportunities in applying artificial intelligence for intelligent fault detection and predictive maintenance in power generation systems.</p>

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Artificial intelligence in power plants: a review of fault detection and predictive maintenance

  • 期刊论文

  • 发表日期：

    2026年6月19日

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  • Nurul Absar,
  • Zannatun Naim Somaya

摘要

The growing complexity and operational requirements of modern power plants demand advanced monitoring and maintenance strategies to maintain system re- liability, efficiency, and safety. Conventional maintenance methods, including re- active and scheduled maintenance, often result in unexpected equipment failures, higher operational expenses, and decreased system availability. In recent years, artificial intelligence (AI) has gained significant attention as an effective approach for intelligent fault detection and predictive maintenance in power generation sys- tems. This review provides a comprehensive analysis of AI-based techniques used for fault diagnosis and predictive maintenance in power plants. The paper explores a range of machine learning and deep learning methods—such as artificial neural networks, support vector machines, decision trees, random forests, and deep neural networks—that are applied to analyze operational data and identify anomalies in critical components, including turbines, boilers, generators, and transformers. Moreover, the study discusses data-driven predictive maintenance frameworks that integrate sensor data, Internet of Things (IoT) technologies, and advanced analytical techniques to predict potential failures before they occur. The advan- tages, limitations, and effectiveness of various AI-based approaches reported in re- cent studies are critically reviewed. In addition, the paper addresses several impor- tant challenges, including data quality issues, model interpretability, computational complexity, and the integration of AI systems with existing industrial infrastruc- tures. Finally, the review outlines potential future research directions aimed at im- proving the robustness and real-world implementation of AI-driven maintenance solutions in power plants. Overall, this study seeks to provide researchers and in- dustry professionals with a comprehensive understanding of recent advancements and emerging opportunities in applying artificial intelligence for intelligent fault detection and predictive maintenance in power generation systems.