This study presents a novel methodology to create an operational Digital Twin for large-scale structures based on drone inspection images. The Digital Twin is primarily used as a virtualized representation of the structure, which will be updated according to physical changes during the life cycle of the structure. The methodology is demonstrated on a wind turbine transition piece. A three-dimensional geometry reconstruction of a transition piece as manufactured is created using a large number (>500) of RGB images collected from a drone and/or several LiDAR scans. Comparing the reconstruction to the original design will locate and quantify geometric deviations and production tolerances. An artificial intelligence algorithm is used to detect and classify paint defects/damages from images. The detected and classified paint defects/damages are subsequently digitalized and mapped to the three-dimensional geometric reconstruction of the structure. These developed functionalities allow the Digital Twin of the structure to be updated with manufacturing-induced geometric deviations and paint defects/damages using inspection images at regular time intervals. The key enabling technologies to realize the Digital Twin are presented in this study. The proposed methodology can be used in different industrial sectors, such as the wind energy, oil, and gas industries, aerospace, the marine and transport sector, and other large infrastructures.

Keywords: Digital Twin; drone; machine learning; 3D reconstruction; damage inspection; artificial intelligence