AI tool locates and classifies defects in wind turbine blades

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.

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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.

Using image enhancement, augmentation methods and the Mask R-CNN deep learning algorithm, the system analyses images, highlights defect areas and labels them.

After developing the system, the researchers tested it by inputting 223 new images. The proposed tool is said to have achieved around 85 per cent test accuracy for the task of recognising and classifying wind turbine blade defects.

The results have been published in a paper published in the Journal of Imaging, which also proposes a new set of measures for evaluating defect detection systems.

In a statement, project leader Dr Cosma said: “AI is a powerful tool for defect detection and analysis, whether the defects are on wind turbine blades or other surfaces.

“Using AI, we can automate the process of identifying and assessing damages, making better use of experts’ time and efforts.

“Of course, to build AI models we need images that have been labelled by engineers, and Railston & Co ltd are providing such images and expertise, making this project feasible.”

Jiajun Zhang added: “Defect detection is a challenging task for AI, since defects of the same type can vary in size and shape, and each image is captured in different conditions [light, shield, image temperature]. The images are pre-processed to enhance the AI-based detection process and currently we are working on increasing accuracy further by exploring improvements to pre-processing the images and extending the AI algorithm.”

Jason Watkins, of Railston & Co Ltd, said the company is “encouraged by the results from the team at Loughborough University”.

He said: “AI has the potential to transform the world of industrial inspection and maintenance. As well as classifying the type of damage we are planning to develop new algorithms that will better detect the severity of the damage as well as the size and its location in space. We hope this will translate into better cost forecasting for our clients.”

As well as further exploring the use of the tech with drone inspections, the Loughborough experts are to build on the research by training the tool to detect the severity of defects. They are also hoping to evaluate the performance of the tool on other surfaces.

This research is funded through the EPSRC Centre for Doctoral Training in Embedded Intelligence, with industrial support from Railston & Co Ltd.