Boralex has worked with SkyVisor to apply artificial intelligence (AI) technology to its wind turbine blade inspections. The case study, covering 49 inspections, examined how the SkyVisor Wind Blade Sense system could improve efficiency and accuracy in identifying blade defects.
The results showed that the AI system achieved a 95% classification accuracy and reduced average inspection time from 22 minutes to about 8.6 minutes per turbine. Across the 49 inspections, 742 defects were analysed, with the AI detecting 60 more critical defects than conventional manual methods. The system is projected to identify an additional 346 defects annually when used across a wider fleet.
The SkyVisor Wind Blade Sense tool uses deep learning models built on convolutional neural networks to classify and locate defects such as cracks, erosion, delamination and lightning strike damage. The system automatically tags images that contain anomalies, allowing inspection teams to focus on affected areas and shorten review times.
SkyVisor’s integrated software suite supports data collection through automated drone flights and links with an asset management platform and field application. This enables centralised record-keeping, quicker maintenance decisions and improved coordination between inspection teams.
The use of AI-driven analysis has helped Boralex streamline its inspection process, enhance defect detection and improve the long-term management of its wind turbine blades.
