Wind turbine blade damage detection using data-driven techniques

This work presents a simple damage detection strategy for wind turbine blades. In particular, a vibration analysis-based damage detection methodology is proposed that requires only healthy data and detects damage in different locations of the blade. The stated structural health monitoring strategy is based on the extraction of characteristics using statistical metrics as a technique for the recognition and differentiation of healthy test experiments from damaged test experiments with simulated faults created by added mass. In this manner, several metrics are approached to find those that show better classification in processing the data provided by the sensors. Finally, an evaluation process is performed to detect blade damage. The results show that the proposed RMSE metric performs at an ideal level, making it a promising strategy for the detection of blade damage.