To deliver a system optimised in the prediction of certain fault types and to further enhance condition monitoring systems to prepare and schedule part replacement before catastrophic failure. The system will reduce operating and maintenance costs for asset owners through the utilisation of an optimal repair schedule.

To embed knowledge in wind turbine condition monitoring, failure prediction, and remaining useful life prediction, based on data driven machine learning and physical modelling approaches, to develop an advanced condition monitoring modelling system for wind turbines.

"The Blade Access System and working Environment (BASE) project will contribute to a 0.6% reduction in the Levelised Cost of Energy (LCoE) from offshore wind by reducing turbine downtime associated with blade maintenance and improving the quality of blade repairs and performance upgrades. The project partners - Span Access, Turner Access, Turner ICENI, Offshore Renewable Energy (ORE) Catapult, Dundee University and Robert Gordon University -- will develop and demonstrate an innovative technology which improves safe and efficient access to wind turbine blades for blade technicians, and creates a more stable working environment for undertaking maintenance which in turn will facilitate a better quality of blade repair. Span Access will lead the project and will collaborate with the partners to optimise their patented TechniSpan technology for the challenge of offshore wind turbine blade access. A prototype BASE unit will be developed and be demonstrated at ORE Catapults 7MW demonstration offshore wind turbine in Levenmouth, Fife. This will be an excellent illustrate of the challenges of working at height on offshore assets where blades which are typically from 50 -- 100m in diameter and 90 metres above the sea."