Artificial Intelligence in Wind Turbine Fault Detection and Diagnosis: Advances and Perspectives

Nejad Alagha, Anis Salwa Mohd Khairuddin (), Zineddine N. Haitaamar, Obada Al-Khatib and Jeevan Kanesan
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Nejad Alagha: Department of Electrical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia
Anis Salwa Mohd Khairuddin: Department of Electrical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia
Zineddine N. Haitaamar: Cumulocity GmbH, Dubai Internet City, Dubai 00000, United Arab Emirates
Obada Al-Khatib: School of Engineering, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai 20183, United Arab Emirates
Jeevan Kanesan: Department of Electrical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia

Energies, 2025, vol. 18, issue 7, 1-23

Abstract: The global shift toward renewable energy, particularly wind power, underscores the critical need for advanced fault diagnosis systems to optimize wind turbine reliability and efficiency. While traditional diagnostic methods remain foundational, their limitations in addressing the nonlinear dynamics and operational complexity of modern turbines have accelerated the adoption of Artificial Intelligence (AI)-driven approaches. This review systematically examines advancements in AI-based fault diagnosis techniques, including machine learning (ML) and deep learning (DL), from 2019 to 2024, analyzing their evolution, efficacy, and practical challenges. Drawing on a curated selection of 55 studies (identified via structured searches across IEEE Xplore, ScienceDirect, and Web of Science), the paper prioritizes research employing data-driven or model-based methodologies with explicit experimental validation and clearly documented data sources. The excluded works lacked English accessibility, validation, or data transparency. Focusing on high-impact faults in gearboxes, blades, and generators, these components are responsible for over 70% of turbine failures, the review maps prevalent ML and DL algorithms, such as CNNs, LSTMs, and SVMs, to specific fault types, revealing hybrid AI models and real-world data integration as key drivers of diagnostic accuracy. Critical gaps are identified, including overreliance on simulated datasets and inconsistent signal preprocessing, which hinder real-world applicability. This study concludes with actionable recommendations for future research, advocating adaptive noise-filtering techniques, scalable hybrid architectures, and standardized benchmarking using operational turbine data. By bridging theoretical AI advancements with practical deployment challenges, this work aims to inform next-generation fault diagnosis systems, enhancing turbine longevity and supporting global renewable energy goals.

Keywords: condition monitoring; deep learning; fault diagnosis; machine learning; wind turbines (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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