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.
Register now to continue reading
Thanks for visiting The Engineer. You’ve now reached your monthly limit of news stories. Register for free to unlock unlimited access to all of our news coverage, as well as premium content including opinion, in-depth features and special reports.
Benefits of registering
-
In-depth insights and coverage of key emerging trends
-
Unrestricted access to special reports throughout the year
-
Daily technology news delivered straight to your inbox
Experts speculate over cause of Iberian power outages
The EU and UK will be moving towards using Grid Forming inverters with Energy Storage that has an inherent ability to act as a source of Infinite...