Abstract
This study investigates Acoustic Emission (AE) measurement data collected in an experiment where fatigue loading tests were carried out on a 14.3-meter full-scale composite wind turbine blade in a test facility. Artificial defects were embedded in the blade and these defects initiate damage growth in four different areas of the blade. Statistical algorithms and techniques were applied to the acoustic emission data to understand the growth of the damages and to identify possible fracture mechanisms. In composite materials, the different types of failure, such as micro-cracking, delamination, matrix cracking, etc. will release AE waveforms with a continuum of frequencies in different ranges. These frequency ranges will be used in the interpretation of the AE data. The AE parameters, i.e., energy and frequency, are used to show a steady damage growth and structural failure of the blade. These parameters, combined with AE amplitude probability distributions, can be used to monitor the structural health of wind turbine blades. This study provides new insights into the acoustic emission characteristics of a composite structure rather than material coupons.
Original language | English |
---|---|
Article number | 115822 |
Journal | Measurement: Journal of the International Measurement Confederation |
Volume | 242 |
Issue number | Part A |
Number of pages | 17 |
ISSN | 0263-2241 |
DOIs | |
Publication status | Published - 2025 |
Keywords
- Damage growth
- Delamination
- Acoustic emission
- Data analysis
- Principal component analysis
- Digital twin