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Artificial Intelligence and Digital Twin Synergy for Predictive Maintenance and Fault Detection in Wind Turbines
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Author(s): Rampelli Manojkumar (BVRIT HYDERABAD College of Engineering for Women, India), Gundu Venu (Malla Reddy (MR) Deemed to be University, India), Suresh Kumar Annam (Malla Reddy University, India), Ramesh Babu Pittala (Anurag University, India), Anji Reddy Polu (BVRIT HYDERABAD College of Engineering for Women, India)and T. Yuvaraj (Chennai Institute of Technology, India)
Copyright: 2026
Pages: 34
Source title:
AI-Powered Analysis, Modeling, and Monitoring of Wind Energy Systems
Source Author(s)/Editor(s): Jackson J. Justo (University of Dar es Salaam, Dar es Salaam, Tanzania & ITMO University, St. Petersburg, Russia), Galina Demidova (Hangzhou Dianzi University, China & ITMO University, St. Petersburg, Russia), Francis A. Mwasilu (University of Dar es Salaam, Tanzania), Dmitry V. Lukichev (ITMO University, Russia)and Ramesh C. Bansal (University of Sharjah, Sharjah, UAE & University of Pretoria, Pretoria, South Africa)
DOI: 10.4018/979-8-3373-4159-0.ch006
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Abstract
This chapter explores the integration of Artificial Intelligence (AI) and Digital Twin (DT) technologies for intelligent condition monitoring and fault detection in wind energy systems. It highlights the limitations of traditional approaches and presents AI-based models for fault prediction, anomaly detection, and remaining useful life estimation. The layered architecture of DTs, their real-time synchronization, and hybrid AI-DT frameworks for predictive maintenance are discussed. The chapter also examines challenges such as data integrity, scalability, and cybersecurity while outlining emerging trends like federated learning and quantum digital twins for resilient and sustainable wind farm operations.
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