Perceptual Robotics previews inspection capabilities

Perceptual Robotics engineers flew an M300 drone to autonomously inspect a G47 wind turbine. (Courtesy: Perceptual Robotics)

Perceptual Robotics has given the wind inspections and maintenance industry a preview of its unique capabilities by holding demonstrations with potential partners.

The company, which has offices in the UK and Europe, welcomed eight companies across Spain to take part in its demonstration day at Sotavento Experimental Wind Farm in Lugo, Spain. Perceptual Robotics engineers flew an M300 drone and used its unique Dhalion system to autonomously inspect a G47 wind turbine at the site.

Two demos were held, with attendees receiving a first-hand preview of the Dhalion system and an inspection as it happened. Perceptual Robotics engineers then showed post flight what data processing looked like and how inspection images and results were presented and analyzed in the system’s web portal.

“This was an excellent opportunity for different stakeholders in the industry to see up close how our system works in real operating conditions. We had people from all aspects of the industry attending, from asset and utility owners to drone companies and inspection organizations. By sharing our extensive experience of inspecting these massive structures, we can bring about the change the industry needs to make inspections more cost effective, timely and safer for all,” said Kostas Karachalios, CEO of Perceptual Robotics.

Perceptual Robotics’ Dhalion system is designed for autonomous in-depth turbine inspections, collecting and analyzing high-quality data from turbines in fewer than 20 minutes.

Earlier this year, the company announced that the advanced technology of robotic systems and artificial intelligence had proven to be almost 15% more accurate in detecting faults in wind turbines thanks to an Innovate UK Research and Development project, which had been ongoing in collaboration between Perceptual Robotics and the University of Bristol. The project showed the partners’ unique system had a 14% improvement in fault detection accuracy when compared with expert humans carrying out the same inspections.