Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset
Creators
- Chaudhuri, Somsubhro (Data manager)1
- Stamm, Michael (Data manager)1
- Lapšanská, Ivana (Data collector)1
- Lançon, Thibault (Data collector)1
- Osterbrink, Lars (Data curator)2
- Driebe, Thomas (Data curator)2
- Hein, Daniel (Data curator)2
- Harendt, René (Data collector)3
- Federal Institute For Materials Research and Testing (Hosting institution)
Description
Abstract
With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.
Files
20240917_REFERENCE_DATASET_README_DETAILED.pdf
Files
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Additional details
Related works
- Is described by
- Journal article: 10.1002/we.2958 (DOI)