Transfer learning-based fault detection in wind turbine blades using radar plots and deep learning models

• Published in: Energy sources. Part A, Recovery, utilization, and environmental effects Recovery, utilization, and environmental effects, 2023-10, Vol.45 (4), p.10789-10801
• Main Authors: Jaikrishna M., Arjun, S, Naveen Venkatesh, V, Sugumaran, Dhanraj, Joshuva Arockia, Velmurugan, Karthikeyan, Sirisamphanwong, Chatchai, Ngoenmeesri, Rattaporn, Sirisamphanwong, Chattariya
• Format: Article
• Language: English
• Subjects: condition monitoring; deep learning; fault identification; transfer learning; Wind turbine blade
• ISSN: 1556-7036; 1556-7230
• DOI: 10.1080/15567036.2023.2246400

Faults in wind turbine blades are considered a critical issue that can affect the safety and performance of wind turbines. The proposed research aimed to monitor wind turbine blades and identify fault conditions using a transfer learning approach. The study utilized one good and four faulty blade conditions: bend, hub-blade loose connection, erosion, and pitch angle twist. Vibration signals for each blade condition were collected and converted as radar plots that were fed and analyzed using pre-trained deep learning models including ResNet-50, AlexNet, VGG-16, and GoogleNet. Hyperparameters including optimizer, train-test split ratio, batch size, epochs, and learning rate were examined to determine the optimal configuration for each network. The study's core findings indicate that ResNet-50 outperformed all other models, achieving an impressive accuracy rate of 99.00%. The other models achieved lower accuracy rates, with AlexNet achieving 96.70%, GoogleNet achieving 97.00%, and VGG-16 achieving 95.00%. These findings highlight the potential of using deep learning models for wind turbine monitoring and fault detection, which could significantly improve the efficiency and reliability of wind turbines.