Integrating AI and Digital Twins for Real-Time Fault Identification in Wind Turbines

The integration of artificial intelligence (AI) and digital twin technologies has revolutionized condition monitoring and fault detection in industrial systems, particularly in wind energy applications. This chapter explores the synergistic relationship between AI algorithms and digital twin frameworks for enhanced predictive maintenance strategies. Digital twins create virtual replicas of physical systems, enabling real-time monitoring, simulation, and analysis of operational conditions. When combined with AI techniques such as machine learning, deep learning, and neural networks, these systems can predict failures, optimize maintenance schedules, and reduce operational costs. The chapter examines current methodologies, implementation challenges, and future prospects of AI-enhanced digital twins in condition monitoring. Case studies from wind turbine applications demonstrate the practical benefits of this integrated approach, showing improvements in fault detection accuracy, reduced downtime, and enhanced system reliability.