Wind Turbine Blade Maintenance through Condition‚ÄêBased Monitoring
James Steck, PhD
James.Steck@wichita.edu
Walter Horn, PhD
Walter.Horn@wichita.edu
Zack Kral
zkral@vt.edu
and
Diagnostics/Prognostics Algorithms for Selected Wind Turbine Components
Haitao Liao, PhD
University of Tennessee –Knoxville
hliao4@utk.edu
The long-term goal of this research is to develop an automated nondestructive condition-based
health monitoring system for composite blades. The one-year goal of this research
is to test the feasibility of integrating structural health monitoring technology
based on AE into wind turbine blades. The outcomes of Subtask 3.3 will be progress
toward damage detection tools for the wind turbine blades. The algorithm created will
be validated by using experimental data on actual wind turbine blade components. This
work is significant because it will reduce the cost of turbine maintenance and downtime
due to inspection processes by improving system health monitoring.
The approach is built earlier results. From these results knowledge on sensor technologies
in nondestructive testing, active and passive ultrasonic sensors are selected. This
system will be examined more thoroughly through experimentation on wind turbine blades
or sections of blades. The required inputs for the chosen prognosis method will be
determined. These experiments will include fatigue testing to accelerate damage growth
and to determine the feasibility of the system under dynamic conditions. A damage
diagnostic tool will be developed using ultrasonic sensor analysis. A novel artificial
intelligence method will be developed as a damage position and size detection system,
based on ultrasonic sensor output.