期刊
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
卷 19, 期 6, 页码 1711-1725出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/1475921719895588
关键词
Passive damage detection; acoustics; wind turbine blades; cracks; holes; delamination; fatigue; cavity
资金
- National Science Foundation [1538100, IIP-1362022]
- Massachusetts Clean Energy Center [23576]
- Aquanis, Inc
- EDP Renewables
- Bachmann Electronic Corp
- GE Energy
- Huntsman
- Hexion
- Leeward Asset Management, LLC
- Pattern Energy
- Texas Wind Tower Corp
- TPI Composites
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1538100] Funding Source: National Science Foundation
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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