"UK policy targets 15% green power generation by 2020 and 57% reduction of CO2 emissions by 2030\. This has led to a significant growth in installed wind power capacity within the last decade (especially in offshore wind power in the UK) to 18.9 GW in the UK and 539 GW globally. The UK market has multiplied 10-fold, supplying 5.4% of the UK's electricity consumption in 2016\.

Turbine blades are subjected to gusting wind loads, driving the accumulation of fatigue damage in the blade structures, leading to failures. Around 3,800 blade failures a year are attributed to poor maintenance. Preventative inspection every 3-4 months and maintenance every 6 months is necessary, costing between £70,000 and £700,000 each. Accidents and fatalities are also quite prevalent: 2,265 accidents to June 2018, with fatalities accounting for 6% and injuries 7%.

This phase 2 further develops RADBLAD, a first-of-its-kind magnetically-adhering wall-climbing robot, with manipulator arm that deploys the x-ray system around a blade. An end effector holds the source and detector against the blade, so they move with the blade in the presence of 3-D blade vibrations. A crucial and novel extension of RADBLAD lies in the use of a radiographic system for inspection and in providing an integrated solution that offers high-quality, efficient inspection method, which is human safe. Unlike radiography, RADBLAD does not require costly, time-consuming onshore dismantling of blades and transportation to workshop, inspection in x-ray bays and return and reassembly, taking around 10-days during which revenue is lost due to generating downtime. Contact methods, e.g. ultrasound volume inspection, are less effective on multi-layered composite structures, and more difficult to perform on-site. RADBLAD is also faster and cheaper than onshore inspection (not counting loss of revenue due to turbine downtime).

To successfully achieve this, the project consortium features the relevant expertise, including robotic development and manufacture, radiography development, and AI algorithm software development.

Our initial target market is the offshore wind turbine operation and maintenance market, with wind farm asset operators the target users. This project represents a clear technological innovation for the UK offshore wind generation industry, and major growth opportunity for the SME supply chain consortium."