Wind turbines are subjected to harsh operating conditions and are subjected to degradation. 20-25% of wind energy cost is due to operations & maintenance [1]. Predicting failures before happening and acting upon this information is essential for reducing the cost of wind energy generation and maintenance, especially for off-shore wind turbines.

 

Designing a reliable prognostics and health management system for wind turbines is very challenging due to the dynamic conditions faced by the turbines.

 

The IMS systematic approach provides a unique and systematic solution for wind turbine Prognostics and Health Management (PHM). In-particular the multi-regime approach for segmenting the data in each operating regime, followed by extracting features from the data and selecting different tools for different purposes.

 

In this demo, we will demonstrate the utilization of different health assessment, diagnostic and prognostic tools for wind turbines. In the demo a sample of gearbox data is used to illustrate the systematic methodology starting with the regime segmentation process, the selection of different prognostic tools and the selection of various information visualization tools.

 

[1]  http://www.ewea.org/fileadmin/ewea_documents/documents/publications/WETF/Facts_Volume_2.pdf