Helitune leads Wind Turbine HUMS collaborative R&D project
Helitune is leading the Wind Turbine HUMS project — a collaborative R&D programme with the National Renewable Energy Centre (Narec) and the University of Bristol, supported by Innovate UK. The project investigates the transfer of condition monitoring technologies and maintenance models from the aerospace industry to the offshore wind turbine sector.
The challenge
Cost-optimised maintenance of wind turbines is increasingly important as offshore wind farms grow in scale and remoteness. Maintenance and repair costs rise significantly due to the specialist vessels required and delays caused by adverse weather conditions — making predictive maintenance a critical capability for the sector.
Offshore wind turbines face a unique combination of challenges. Unlike helicopter operators, who can elect to avoid harsh conditions, wind turbines are continuously exposed to dynamic loads from rapid changes in wind speed and direction, yaw misalignment, wind shear across blade diameter and turbulent wakes from upstream turbines. These conditions introduce unsteady loads that can excite vibrations in critical components and accelerate fatigue.
The solution
The Wind Turbine HUMS solution draws on techniques developed by Helitune for the helicopter industry, adapted to measure the impact of rapid wind fluctuations and assess their effect on the remaining life of key components. The University of Bristol has adapted algorithms from the aerospace and medical industries to detect transient events in vibration signals, and these solutions are being tested and evaluated at Narec's facilities in Blyth, UK.
The system enables predictive maintenance and component life extension through advanced maintenance protocols, minimising risk for insurers and reducing the levelised cost of energy.
Results to date
Helitune presented a paper at the European Wind Energy Association (EWEA) in Barcelona, sharing results from the first stage of Narec testing. The paper demonstrated classification of extreme wind events using wind turbine vibration signals — showing that monitoring individual turbine exposure to extreme events can identify turbines at greater risk of fatigue due to localised wind conditions or array position, and that comparing data across a fleet over time can inform a predictive maintenance strategy.
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