DNV Partnership To Explore Expanded Use of Drones To Collect Wind Turbine Data
The risk-management firm is teaming up with academia and a robotics specialist for a research project focused on automatic processing of data gathered by autonomous and remote-controlled vehicles.
DNV has partnered with the University of Bristol and Perceptual Robotics to launch a new, grant-supported collaborative research project to develop an automated data-processing procedure for verification of detected wind turbine blade defects. The project aims to inform future regulation as well as to build trust and generate broader acceptance of automated data-processing techniques across the industry.
The project will investigate the automated verification, validation, and processing of inspection data collected by autonomous drones to improve inspection quality and performance.
Unmanned autonomous and remote-controlled vehicles and drones are routinely used to conduct asset inspections in the hard-to-reach, extreme environments of offshore wind farms. These vehicles can collect extensive datasets including high-definition video, images, and geopositioning and sensor data to provide structural integrity information without personnel having to access these dangerous locations.
The project will run for 12 months from April 2021 and will address the need for fully automated processing of the data collected, where currently this remains a semiautomated process with reliance on visual inspections of image data by trained experts.
“With many inspections still being carried out manually, visual inspection of offshore wind turbines is expensive, labor intensive, and hazardous,” said Elizabeth Traiger, a DNV senior researcher in digital assurance. “Automatic visual inspections can address these issues. This collaboration will develop and demonstrate an automated processing pipeline alongside a general framework with the aim of generating broader acceptance across the industry and informing future regulation. This project should provide a stepping stone to the growth of the automated inspection industry.”
With the number of installed wind turbines worldwide increasing, including those in remote and harsh environments, the volume of inspection data collected is quickly outpacing the capacity of skilled inspectors who can competently review it. As part of the project, the Visual Information Lab at the University of Bristol, experts in 3D computer vision and image processing, will create algorithms for automated localization of inspection images and defects.
Perceptual Robotics, specializing in visual inspection of wind turbines using drones, will perform drone inspections and create AI-based models for defect detection to trial automation of process in a commercial production environment.
DNV will provide inspection expertise, verify data collected, validate the methodology and performance of the AI algorithms, and provide guidance as to existing DNV and IEC recommended practices, regulations, and industry networks.
The 2021 Offshore Technology Conference in Houston will include technical sessions and panel discussions about wind energy and robotics.