Condition Monitoring and Fault Detection With AI and Digital Twin Technologies

As the world moves toward renewable energy, wind power stands out as a key sustainable source. However, wind turbines face challenges in reliability due to harsh conditions, mechanical wear, and electrical stress. Traditional maintenance falls short for large wind farms. This chapter explores how Artificial Intelligence (AI) and Digital Twin technologies enable real-time monitoring of blades, gearboxes, bearings, generators, and converters. AI models like Neural Networks, Support Vector Machines, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) help detect faults and predict failures early. Digital twins enhance diagnostics by simulating turbine behavior using real-time data. The chapter also covers hybrid modeling, anomaly detection, and federated learning. Case studies include offshore wind AI, blade crack detection, and converter diagnostics. Challenges include data quality, cybersecurity, and scalability. Future trends point to edge AI, cloud-based diagnostics, and blockchain for secure monitoring and predictive maintenance.

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