Acoustic emission data analytics on delamination growth in a wind turbine blade under full-scale cyclic testing
Hans‐Henrik von Benzon, Adam Mielke, Tobias Ritschel, Malcolm McGugan, Kim Branner, Xiao Chen
Abstract
• Comprehensive acoustic emission data analysis provides a new understanding of damage progress in a full-scale wind turbine blade. • How well the best-fitted Weibull distributions follow the measured MARSE energy probability densities can, therefore, provide an indication of the damage state. • Color plots of the MARSE energy as a function time and average frequency show the location of the different damage types in the composite structure. 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.