Aug 2026· e-Journal of Nondestructive Testing· Vol 31· 0 citations
Abstract
In 2025, wind energy was the primary source of renewable electricity in the EU. As wind energy deployment continues, automatic inspection of wind turbines, especially rotor blades, has gained increasing interest for the economically competitive wind energy market. Therefore, this study introduces a three-stage framework for combined damage detection and localization in wind turbine rotor blades based on acoustic event detection. Bridging the gap between established SHM techniques analyzing either low-frequency vibrations or ultrasonic structure-borne sound waves, our methodology is based on airborne sound in the audible frequeny range. First, we apply an adaptive thresholding technique to extract short-term percussive sound signals, which are subsequently localized using Time Differences of Arrival. Finally, a spatio-temporal accumulation of the emitted acoustic energy is introduced as a damage indicator. Our evaluations on two large-scale rotor blade fatigue tests demonstrate that a configuration of only two microphones is sufficient to detect and coarsely localize structurally relevant damage within a 12-m-long blade segment. To the best of the authors' knowledge, such a favorable trade-off between sensor spacing and localization accuracy remains unprecedented among established structure-borne SHM techniques.
Wind energy, as a major contributor to the renewable energy sector, is receiving increasing attention to meet the growing demand for low-carbon electricity. The performance of a wind turbine can be significantly affected by various types of damage in its components, particularly the rotor blades. If left undetected, da...
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The rapid expansion of offshore wind farms (OWFs) is reshaping coastal soundscapes through operational turbine noise, while the acoustic responses of soniferous fishes within operational wind-farm areas remain difficult to assess using conventional fishery surveys. In this study, passive acoustic monitoring (PAM) was d...