Three-Dimensional Defect Measurement and Analysis of Wind Turbine Blades Using Unmanned Aerial Vehicles

• Published in: Drones (Basel) Vol. 9; no. 5; p. 342
• Main Authors: Hung, Chin-Yuan, Chu, Huai-Yu, Wang, Yao-Ming, Wen, Bor-Jiunn
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: Air-turbines; Algorithms; Automation; Blades; Cameras; Composite materials; Defects; Drone aircraft; Heat detection; Image quality; Image reconstruction; Infrared imagery; Inspection; instant neural graphic primitives; Methods; neural radiance field; Nondestructive testing; Orthography; Preventive maintenance; Processing speed; thermal image defect; Thermal imaging; Thermography; Three dimensional analysis; three-dimensional image reconstruction; Turbine blades; Turbines; unmanned aerial vehicle; Unmanned aerial vehicles; Wind power; Wind power generation; wind turbine blade; Wind turbines; Taiwan
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones9050342

Wind turbines’ volume and power generation capacity have increased worldwide. Consequently, their inspection, maintenance, and repair are garnering increasing attention. Structural defects are common in turbine blades, but their detection is difficult due to the relatively large size of the blades. Therefore, engineers often use nondestructive testing. This study employed an unmanned aerial vehicle (UAV) to simultaneously capture visible-light and infrared thermal images of wind power blades. Subsequently, instant neural graphic primitives and neural radiance fields were used to reconstruct the visible-light image in three dimensions (3D) and generate a 3D mesh model. Experiments determined that after converting parts of the orthographic-view images to elevation- and depression-angle images, the success rate of camera attitude calculation increased from 85.6% to 97.4%. For defect measurement, the system first filters out the perspective images that account for 6–12% of the thermal image foreground area, thereby excluding most perspective images that are difficult to analyze. Based on the thermal image data of wind power generation blades, the blade was considered to be in a normal state when the full range, average value, and standard deviation of the relative temperature grayscale value in the foreground area were within their normal ranges. Otherwise, it was classified as abnormal. A heat accumulation percentage map was established from the perspective image of the abnormal state, and defect detection was based on the occurrence of local minima. When a defect was observed in the thermal image, the previously reconstructed 3D image was switched to the corresponding viewing angle to confirm the actual location of the defect on the blade. Thus, the proposed 3D image reconstruction process and thermal image quality analysis method are effective for the long-term monitoring of wind turbine blade quality.