Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms

Bridger Altice, Edwin Nazario, Mason Davis, Mohammad Shekaramiz (), Todd K. Moon and Mohammad A. S. Masoum
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Bridger Altice: Information Dynamics Lab., Electrical and Computer Engineering Department, Utah State University, Logan, UT 84322, USA
Edwin Nazario: Machine Learning and Drone Lab., Engineering Department, Utah Valley University, Orem, UT 84097, USA
Mason Davis: Machine Learning and Drone Lab., Engineering Department, Utah Valley University, Orem, UT 84097, USA
Mohammad Shekaramiz: Machine Learning and Drone Lab., Engineering Department, Utah Valley University, Orem, UT 84097, USA
Todd K. Moon: Information Dynamics Lab., Electrical and Computer Engineering Department, Utah State University, Logan, UT 84322, USA
Mohammad A. S. Masoum: Machine Learning and Drone Lab., Engineering Department, Utah Valley University, Orem, UT 84097, USA

Energies, 2024, vol. 17, issue 5, 1-21

Abstract: Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and outdoor images of a small wind turbine with healthy and damaged blades. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception outperformed other architectures by attaining 99.92% accuracy on the test data of this dataset. Furthermore, the performance of the investigated models was compared on a dataset containing faulty and healthy images of large-scale wind turbine blades. In this case, our results indicated that the best-performing model was also the proposed Transfer Xception, which achieved 100% accuracy on the test data. These accuracies show promising results in the adoption of machine learning for wind turbine blade fault identification.

Keywords: deep learning; convolutional neural networks; small wind turbine blades; damage detection; transfer learning; VGG-19; Xception; AlexNet; ResNet-50; aerial imaging; turbine inspection (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: 2024
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