AI-based Multi-modal Non-Destructive Aerial Inspection System and Digital Twin of Wind Farms
The AeroNeX project adopts an advanced scientific and technical approach that combines autonomous aerial robotics, multi-modal non-destructive testing (NDT) and AI-based data fusion with digital twin technology to create a breakthrough solution for predictive maintenance of wind turbine infrastructure. The project introduces a novel method for defect detection by integrating Ground Penetrating Radar (GPR) and X-Ray imaging with optical inspection technologies, enabling comprehensive internal and external assessment of wind turbine blades, towers, and foundations. This combination of subsurface and surface inspection capabilities marks a significant leap beyond the current state of the art, offering a new standard for autonomous infrastructure inspection in the wind energy sector.
This project brings together a consortium consisting of NORCE and Simis from Norway, and SpectX from the Netherlands, to create a unique international collaboration of industrial expertise, applied research capabilities and software innovation.
Objectives:
- Develop an autonomous, multi-sensor aerial inspection system that combines GPR, X-Ray imaging, and visual inspection technologies to detect both visible external defects and hidden surface abnormalities.
- Process the collected data using AI models to automatically detect, classify, and segment defects to lower inspection times.
- Develop a fully integrated digital twin model that can enable predictive maintenance and lifetime extension of turbine components through a data-analytics platform that processes inspection data and generates actionable insights.