DNV Launches New Research Project for Automated Verification of Offshore Wind Turbine Inspection Results

DNV has launched a new collaborative research project in partnership with the University of Bristol and Perceptual Robotics, to develop an automated data processing procedure for verification of detected wind turbine blade defects.

May 15, 2021. By Manu Tayal

DNV has launched a new collaborative research project in partnership with the University of Bristol and Perceptual Robotics, to develop an automated data processing procedure for verification of detected wind turbine blade defects.

The project, which will run for 12 months from April 2021, aims to build trust and generate broader acceptance of automated data processing techniques across the industry and to inform future regulation.

Moreover, the research project will investigate the automated verification, validation and processing of inspection data, collected by autonomous drones, to improve inspection quality and performance. It aimed to contribute to the development of the UK automated inspection industry.

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.”

“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,” added Pierre C Sames, Group Research and Development Director at DNV.

As part of the project, the Visual Information Lab at the University of Bristol will create algorithms for automated localisation of inspection images and defects using SLAM and 3-D tracking technology.

Perceptual Robotics will perform drone inspections and create AI based models for defect detection to trial automation of process in a commercial production environment.

Meanwhile, 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 recommend practices, regulations and industry networks.

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