They used AI to establish cause and effect of edge erosion of a turbine blade, simulated potential fixes and determined the best solution.
The researchers then used 3D printing to create a material capable of hardening under mechanical stress and which is more resistant to edge erosion.
Leading edge erosion, which can be caused by rain and sea water, affects turbine production. Repairs and replacements are costly and result in long periods of downtime.
"People's ability to perceive is not enough to see all the dimensions involved in optimizing the material solution.
"Artificial intelligence, on the other hand, is capable of breaking down very complex cause-effect relationships, simulating solutions, and finding the infinite options that work best with the demands," said VTT senior research Anssi Laukkanen.
Researchers took under a year to use virtual testing and machine learning to develop the solution at the centre in Espoo in the south of Finland.
Laukkanen added VTT was now discussing findings with turbine manufacturers.
VTT is also seeking further funding for the project and intends to expand its scope to other industrial sectors.
To learn more about blade maintenance, check out Windpower Monthly’s Blade O&M Europe Forum (12-14 March 2019) in Amsterdam, the Netherlands.