ACOUSTIC
EMISSION & NDT SCIENTIFIC PUBLICATIONS
Unsupervised Pattern Recognition of Acoustic Emission
from Full Scale Testing of a Wind Turbine Blade
J. of Acoustic Emission, Vol. 18, 2000, pp 217-223
(Initially presented and published at the proceedings of EWGAE
2000 - 24th European Conference on AE Testing, CETIM
- France, 24-26 May, 2000, pp. 291-297)
D.
Kouroussis, A.
Anastasopoulos,
P. Vionis, V. Kolovos
Acoustic
Emission (AE) monitoring during full scale testing of FRP
Wind Turbine (W/T) blades is a, relatively, new application.
The difficulty in such tests arises from the potentiality of
different AE sources expected, due to the nature of FRP
materials, as well as the complex design of W/T blades. AE
data obtained during a static proof test of a 12m FRP Wind
Turbine blade was analyzed, in order to assess the
criticality of specific AE sources. Unsupervised pattern
recognition (UPR) was used to segregate the AE signals into
various classes, based on the similarity of AE features. The
yielded classes were, then, correlated with the applied load,
and the AE characteristics of each class were compared.
Particular classes were observed to appear at early load
stages, but ceased at higher loads, while some classes were
considerably active during high loads and load-holds. The
application of UPR on such AE data was proved to be a
powerful tool towards the characterization of damage
evolution with increasing load. Combined use of UPR and
location of particular AE sources, located with traditional
location algorithms was investigated, revealing that a
localized source can yield more than one classes as the
damage mechanism associated with it might change in time and
with respect to the applied load.