A Novel Approach for Defect Detection of Wind Turbine Blade Using Virtual Reality and Deep Learning

• URL: http://arxiv.org/abs/2401.00237v1
• Date: Sat, 30 Dec 2023 13:58:50 GMT
• Title: A Novel Approach for Defect Detection of Wind Turbine Blade Using Virtual Reality and Deep Learning
• Authors: Md Fazle Rabbi, Solayman Hossain Emon, Ehtesham Mahmud Nishat, Tzu-Liang (Bill) Tseng, Atira Ferdoushi, Chun-Che Huang and Md Fashiar Rahman
• Abstract summary: We develop virtual models of wind turbines to synthesize the near-reality images for four types of common defects. In the second step, a customized U-Net architecture is trained to classify and segment the defect in turbine blades. The proposed methodology provides reasonable defect detection accuracy, making it suitable for autonomous and remote inspection through aerial vehicles.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wind turbines are subjected to continuous rotational stresses and unusual external forces such as storms, lightning, strikes by flying objects, etc., which may cause defects in turbine blades. Hence, it requires a periodical inspection to ensure proper functionality and avoid catastrophic failure. The task of inspection is challenging due to the remote location and inconvenient reachability by human inspection. Researchers used images with cropped defects from the wind turbine in the literature. They neglected possible background biases, which may hinder real-time and autonomous defect detection using aerial vehicles such as drones or others. To overcome such challenges, in this paper, we experiment with defect detection accuracy by having the defects with the background using a two-step deep-learning methodology. In the first step, we develop virtual models of wind turbines to synthesize the near-reality images for four types of common defects - cracks, leading edge erosion, bending, and light striking damage. The Unity perception package is used to generate wind turbine blade defects images with variations in background, randomness, camera angle, and light effects. In the second step, a customized U-Net architecture is trained to classify and segment the defect in turbine blades. The outcomes of U-Net architecture have been thoroughly tested and compared with 5-fold validation datasets. The proposed methodology provides reasonable defect detection accuracy, making it suitable for autonomous and remote inspection through aerial vehicles.

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