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Cavities with different geometries represent the internal volumes of various engineering applications such as cabins of passenger cars, fuselages and wings of aircraft, and internal compartments of wind turbine blades. Transmissibility of acoustic excitation to and from these cavities is affected by material and cross-sectional properties of the structural cavity, as well as potential damage incurred. A new structural damage detection methodology that relies on the detectability of the changes in acoustic transmissibility across the boundaries of structural cavities is proposed. The methodology is described with a specific focus on the passive damage detection approach applied to cavity internal acoustic pressure responses under external flow-induced acoustic excitations. The approach is realized through a test plan that considers a wind turbine blade section subject to various damage types, severity levels, and locations, as well as wind speeds tested in a subsonic wind tunnel. A number of statistics-based metrics, including power spectral density estimates, band power differences from a known baseline, and the sum of absolute difference, were used to detect damage. The results obtained from the test campaign indicated that the passive acoustic damage detection approach was able to detect all considered hole-type damages as small as 0.32 cm in diameter and crack-type damages 1.27 cm in length. In general, the ability to distinguish damage from the baseline state improved as the damage increased in severity. Damage type, damage location, and flow speed influenced the ability to detect damage, but were not significant enough to prevent detection. This article serves as an overall proof of concept of the passive-based damage detection approach using flow-induced acoustic excitations on structural cavities of a wind turbine blade. The laboratory-scale results reveal that acoustic-based monitoring has great potential to be used as a new structural health monitoring technique for utility-scale wind turbine blades.
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An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades
This article details the implementation of a novel passive structural health monitoring approach for damage detection in wind turbine blades using airborne sound. The approach utilizes blade-internal microphones to detect trends, shifts, or spikes in the sound pressure level of the blade cavity using a limited network of internally distributed airborne acoustic sensors, naturally occurring passive system excitation, and periodic measurement windows. A test campaign was performed on a utility-scale wind turbine blade undergoing fatigue testing to demonstrate the ability of the method for structural health monitoring applications. The preliminary audio signal processing steps used in the study, which were heavily influenced by those methods commonly utilized in speech-processing applications, are discussed in detail. Principal component analysis and K-means clustering are applied to the feature-space representation of the data set to identify any outliers (synonymous with deviations from the normal operation of the wind turbine blade) in the measurements. The performance of the system is evaluated based on its ability to detect those structural events in the blade that are identified by making manual observations of the measurements. The signal processing methods proposed within the article are shown to be successful in detecting structural and acoustic aberrations experienced by a full-scale wind turbine blade undergoing fatigue testing. Following the assessment of the data, recommendations are given to address the future development of the approach in terms of physical limitations, signal processing techniques, and machine learning options.
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- Award ID(s):
- 1916715
- PAR ID:
- 10547266
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Structural Health Monitoring
- Volume:
- 19
- Issue:
- 6
- ISSN:
- 1475-9217
- Format(s):
- Medium: X Size: p. 1711-1725
- Size(s):
- p. 1711-1725
- Sponsoring Org:
- National Science Foundation
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