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Mapping Damages from Inspection Images to 3D Digital Twins of Large-Scale Structures
  • Hans-Henrik von Benzon,
  • Xiao Chen
Hans-Henrik von Benzon
Technical University of Denmark
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Xiao Chen
Technical University of Denmark

Corresponding Author:xiac@dtu.dk

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Abstract

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.
21 Jul 2023Submitted to Engineering Reports
25 Jul 2023Submission Checks Completed
25 Jul 2023Assigned to Editor
25 Jul 2023Review(s) Completed, Editorial Evaluation Pending
27 Jul 2023Reviewer(s) Assigned
28 Aug 2023Editorial Decision: Revise Major
23 Sep 20231st Revision Received
25 Sep 2023Assigned to Editor
25 Sep 2023Submission Checks Completed
25 Sep 2023Review(s) Completed, Editorial Evaluation Pending
27 Sep 2023Reviewer(s) Assigned
19 Oct 2023Editorial Decision: Revise Minor
26 Oct 20232nd Revision Received
02 Nov 2023Assigned to Editor
02 Nov 2023Submission Checks Completed
02 Nov 2023Review(s) Completed, Editorial Evaluation Pending
06 Nov 2023Reviewer(s) Assigned