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Title: Active acoustic damage detection of structural cavities using internal acoustic excitations
A novel structural damage detection methodology that relies on the detectability of the changes in acoustic transmissibility across boundaries of structural cavities is investigated. The approach focuses on active damage detection by leveraging the acoustic pressure responses measured external to structural cavities while exposed to internal acoustic excitations. The active damage detection concept is first demonstrated on a 4 m wind turbine blade using acoustic beamforming techniques to confirm that the acoustic energy transmitted through a damaged surface increases local to the damage compared to an undamaged surface. The concept is further verified, only considering acoustic pressure responses measured from limited microphones positioned at various distances from a ~46 m wind turbine blade. A comprehensive testing campaign is developed and executed on the utility-scale blade considering various damage types, severity levels, and locations. The data are analyzed using a combination of spectral analysis and statistics-based metrics to detect and track the progression of damage as well as identify trends across the test variables. Overall, large increases in the power spectral density were observed from the pressure responses measured external to the structure in most cases. The spectral differences increased as the damage became more severe and damage as small as 5.1 cm in length was easily detected from multiple sensors up to 17.1 m from the damage location. Damage was easily detected when implemented before the mid-length of the blade using simple signal processing algorithms and preliminary test configurations. The data acquired in this work serve as a preliminary investigation into the capability of the approach on complex structures and paves the path for future research into the signal processing techniques and test configurations that will enhance the performance of the active acoustic damage detection approach.  more » « less
Award ID(s):
1916715
NSF-PAR ID:
10203413
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Structural Health Monitoring
Volume:
19
Issue:
1
ISSN:
1475-9217
Page Range / eLocation ID:
48 to 65
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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