DNV and partners launch new research project to develop automated verification of offshore wind turbine inspection results
01/06/2021
DNV has launched a new collaborative research project to develop an automated data processing procedure for verification of detected wind turbine blade defects, with the aim of building trust and generating broader acceptance of automated data processing techniques across the industry and to inform future regulation.
The research project, which is being conducted in partnership with the University of Bristol and Perceptual Robotics, will investigate the automated verification, validation and processing of inspection data collected by autonomous drones to improve inspection quality and performance. The project aims to contribute to the development of the UK automated inspection industry.Unmanned autonomous and remote-controlled vehicles and drones are routinely used to conduct asset inspections in the hard-to-reach and extreme environments of offshore wind farms. These vehicles can collect rich and extensive datasets, including high-definition video, images, geopositioning and sensor data, to provide integrity information about the installed structures without personnel having to access these dangerous locations.
The research project, which will run for 12 months from April 2021, will address the need for fully automated processing of the data collected, where currently this remains a semi-automated process with reliance on visual inspections of image data by trained experts.
Dr Elizabeth Traiger, a DNV Senior Researcher in digital assurance, said: “With many inspections still being carried out manually, visual inspection of offshore wind turbines is expensive, labour-intensive and hazardous. 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.”
Pierre C Sames, Group Research and Development Director at DNV, added: “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. This research project will develop means to tackle this challenge through machine learning algorithms and process automation.”
As part of the project, the Visual Information Laboratory at the University of Bristol, with expertise in 3D computer vision and image processing, will create algorithms for automated localisation of inspection images and defects using simultaneous localisation and mapping (SLAM) and 3D tracking technology.
Perceptual Robotics, a small-/medium-sized enterprise (SME)specialising in visual inspection of wind turbines using drones, will perform drone inspections and create artificial intelligence (AI)-based models for defect detection to trial automation of the 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 research is supported by an Innovate UK grant gained as a result of winning the ‘Robotics for a Safer World’ extension competition.
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