Offshore wind energy plays a critical role in the global renewable energy transition, yet its maintenance remains costly and hazardous. VAWIS addresses these challenges by integrating advanced drone and sensor technologies with AI-powered analytics and digital twin modeling to redefine inspection processes.

The system employs autonomous UAVs that operate from mobile docking stations on boats rather than turbine-mounted platforms. These drones are equipped with hyperspectral cameras, RGB sensors, and thermal imaging systems to perform routine inspections regardless of weather conditions or GNSS availability. Sensor fusion technology ensures precise detection of defects such as corrosion and structural damage.

AI-driven predictive maintenance models analyze collected data to identify potential failure points, enabling proactive maintenance decisions. The digital twin framework replicates turbine conditions, enhancing inspection accuracy by 40% compared to conventional UAVs and reducing maintenance costs by 40%.

The implementation of automated docking stations minimizes human intervention by 60%, enhancing operational efficiency and safety. These docking units are designed for offshore conditions and support seamless, autonomous drone operations.

The VAWIS project targets offshore wind farms primarily but extends its applications to oil & gas platforms and maritime infrastructure. By demonstrating the system at TRL 6 by the project’s end, VAWIS aims to present a scalable, market-ready solution that aligns with industry demands for reliable, cost-effective offshore asset inspection solutions.

Stay tuned for more information, or inquire now.