AI-Driven Multimodal Spectroscopic Imaging for Wind Turbine Blade Health Monitoring
AI-Driven Multimodal Spectroscopic Imaging for Wind Turbine Blade Health Monitoring
Name of applicant
Shohreh Sheiati
Title
Postdoctoral Fellow
Institution
KTH Royal Institute of Technology
Amount
DKK 2,700,382
Year
2026
Type of grant
Internationalisation Fellowships
What?
Wind turbines supply ~60% of Denmark’s electricity, generating over DKK 100 billion in revenue. ~10-25% of this amount is spent on operations and maintenance, of which wind turbine blade failures account for ~65%. Blades gradually degrade over time, eventually leading to visible damage. This project aims to detect these early changes before damage occurs.
Why?
Early detection of blade degradation can significantly reduce maintenance costs, prevent failures, and improve the reliability of wind energy. By identifying problems before they become critical, this research supports more sustainable and cost-effective renewable energy production.
How?
This project combines advanced imaging and artificial intelligence to detect subtle changes in blade materials. By linking laboratory measurements with large-scale imaging, the developed approach will enable rapid, non-destructive monitoring of wind turbine blades under real operating conditions.