Mapping damages from inspection images to 3D digital twins of large‐scale structures
में बचाया:
| में प्रकाशित: | Engineering Reports vol. 7, no. 1 (Jan 1, 2025) |
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| मुख्य लेखक: | |
| अन्य लेखक: | |
| प्रकाशित: |
John Wiley & Sons, Inc.
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| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full Text Full Text - PDF |
| टैग: |
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|---|---|---|---|
| 001 | 3161574840 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2577-8196 | ||
| 024 | 7 | |a 10.1002/eng2.12837 |2 doi | |
| 035 | |a 3161574840 | ||
| 045 | 0 | |b d20250101 | |
| 100 | 1 | |a von Benzon, Hans‐Henrik |u Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark | |
| 245 | 1 | |a Mapping damages from inspection images to 3D digital twins of large‐scale structures | |
| 260 | |b John Wiley & Sons, Inc. |c Jan 1, 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. 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 3D 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. The 3D model can here both be a CAD model of the structure or a 3D reconstruction based on photogrammetry. 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. | |
| 653 | |a Digital mapping | ||
| 653 | |a Digital imaging | ||
| 653 | |a Deep learning | ||
| 653 | |a Nondestructive testing | ||
| 653 | |a Wind farms | ||
| 653 | |a Rotor blades | ||
| 653 | |a Offshore structures | ||
| 653 | |a Damage detection | ||
| 653 | |a Cracks | ||
| 653 | |a Energy consumption | ||
| 653 | |a Three dimensional composites | ||
| 653 | |a Wind damage | ||
| 653 | |a Internet of Things | ||
| 653 | |a Photogrammetry | ||
| 653 | |a Rotor blades (turbomachinery) | ||
| 653 | |a Turbines | ||
| 653 | |a Methodology | ||
| 653 | |a Inspection | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Wind power | ||
| 653 | |a Image segmentation | ||
| 653 | |a Digital twins | ||
| 653 | |a Neural networks | ||
| 653 | |a Sensors | ||
| 653 | |a Three dimensional models | ||
| 653 | |a Wind turbines | ||
| 653 | |a Drones | ||
| 653 | |a Computer aided design--CAD | ||
| 653 | |a Algorithms | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Asset management | ||
| 653 | |a Industry 4.0 | ||
| 700 | 1 | |a Chen, Xiao |u Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark | |
| 773 | 0 | |t Engineering Reports |g vol. 7, no. 1 (Jan 1, 2025) | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3161574840/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3161574840/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3161574840/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |