DNV & Co Developing Automated Verification of Offshore Wind Turbine Inspection Results

DNV, in partnership with the University of Bristol and Perceptual Robotics, has launched a new research project to develop automated verification of offshore wind turbine inspection results.

The project will develop an automated data processing procedure for verification of detected wind turbine blade defects. It will investigate the automated verification, validation and processing of inspection data, collected by autonomous drones, to improve inspection quality and performance.

The project, which will run for a year, aims to contribute to the development of the UK automated inspection industry, DNV said.

Unmanned autonomous and remote-controlled vehicles and drones, used to conduct asset inspections in the hard-to-reach and extreme environments of offshore wind farms, can collect rich and extensive data sets, including high-definition video, images, geo-positioning and sensor data, to provide integrity information about the installed structures without personnel having to access these dangerous locations. The research project 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, according to DNV.

“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”, said 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.

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.