Mapping Damages from Inspection Images to 3D Digital Twins of Large-Scale Structures

This study develops a methodology to create detailed visual Digital Twins of large-scale structures with their realistic damages detected from visual inspection or nondestructive testing (NDT). The methodology is demonstrated with a transition piece of an offshore wind turbine and a composite rotor blade, with surface paint damage and subsurface delamination damage, respectively. Artificial Intelligence and color threshold segmentation are used to classify and localize damages from optical images taken by drones. These damages are digitalized and mapped to a three-dimensional geometry reconstruction of the large-scale structure or a CAD model of the structure. To map the images from 2D to 3D, metadata information is combined with the geo placement of the large-scale structure’s 3D model. After mapping the damage, the Digital Twin gives an accurate representation of the structure. The location, shape, and size of the damage are visible on the Digital Twin. The demonstrated methodology can be applied to industrial sectors such as wind energy, the oil and gas industry, marine and aerospace to facilitate asset management.