ACOUSTIC
EMISSION & NDT SCIENTIFIC PUBLICATIONS
Analysis of Acoustic
Emission Data from Wind Turbine Blade Testing Using
Unsupervised Pattern Recognition
15th International
Acoustic Emission Symposium, Tokyo, Japan, 11-14 Sept.
2000
A. A.,
Anastassopoulos, S. J., Vahaviolos, D. A. Kouroussis, P.
Vionis, J. C. Lenain, A. Proust
Acoustic
Emission testing of FRP structures, such as Wind Turbine blades,
is a challenging application because the blades themselves are
of complex design, and FRP materials are, by nature, emissive,
when subjected to loading. During loading of a blade, different
damage mechanisms, which produce AE, might be initiated or
ceased at different load levels and at different sections of the
blade, or even coexist. Current Wind Turbine blade certification
practices are based solely on visual inspection and heuristic
quantification of audible damage indications. Therefore,
segregation of the damage mechanisms and characterization of
their criticality with load, by means of AE, is essential for
both the structural integrity assessment of the blade and the
understanding of the damage evolution with increasing load. In
the present work, similar Wind Turbine blades were tested with
AE, at different loading envelopes and load levels. Unsupervised
Pattern Recognition analysis was performed on the corresponding
Acoustic Emission data which were clustered by UPR, based on
their AE characteristics. The resulting clusters of data were
compared with respect to their criticality, AE features and
location on the blade. Particular clustering algorithms were
proved to be very efficient in discriminating the various AE
mechanisms for each test case, while clusters with similar AE
characteristics appeared in different tests. Overall, UPR
analysis proved to be a powerful tool towards the evaluation and
physical interpretation of AE data.