Classification Analytics for Wind Turbine Blade Faults

• Date: Thursday, November 14th
• Time: 11:30-12:00
• Place: Webinar
  To get access, please contact CIAM, Odd Terje Høie on E-mail: odd.t.hoie@uis.no

Wind turbine blades are critical components of wind energy systems, and their structural health is essential for reliable operation and maintenance. Several studies have predicted wind turbine blade faults, like erosion and cracks, using time-domain and frequency-domain features alongside machine learning techniques. Integrating these methods into a unified framework for fault classification might offer a more comprehensive solution. In this "Lunch & Learn", the integrated approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine-learning techniques will be presented.  Results will highlight and compare different machine learning algorithms in terms of accuracy and error quantifications. 

Presenter Ali Waqar