AI tool locates and classifies defects in wind turbine blades
Computer scientists at Loughborough University have developed a new tool that uses AI to analyse images of wind turbine blades to locate and highlight defects.
The system has been ‘trained’ to classify defects by type – such as crack, erosion, void, and ‘other’ – which could lead to faster and more targeted responses.
BladeBUG makes robotic ‘blade walk’ on operational wind turbine
Current methods of inspection require engineers to carry out manual examinations, which entails capturing a large number of high-resolution images. These inspections are time-consuming, impacted by light conditions and can be hazardous.
The proposed tool can currently analyse images and videos captured from inspections that are carried out manually or with drones. Future research will further explore using the AI tool with drones, eliminating manual inspections altogether.
Research leads Dr Georgina Cosma and PhD student Jiajun Zhang trained the AI system to detect different types of defects using a dataset of 923 images captured by Railston & Co Ltd, the project’s industrial partner.
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