ACOUSTIC
EMISSION & NDT SCIENTIFIC PUBLICATIONS
Structural Integrity Evaluation Of Wind Turbine Blades Using
Pattern Recognition Analysis On Acoustic Emission Data
J. of Acoustic Emission, Vol.
20, 2002, pp. 229-237
(Initially
presented and published at the
proceedings of the 25th European Conference on
Acoustic Emission Testing - EWGAE 2002, September 11 – 13, 2002,
Prague, Czech Republic, Editor: P. Mazal, ISBN 80-214-2174-6,
Volume I, pp. 21-28)
A.
A. Anastasopoulos, D. A. Kouroussis, V. N. Nikolaidis, A. Proust,
A. G. Dutton, M. Blanch, L. E. Jones, P. Vionis, D. J. Lekou, D
R V van Delft, P. A. Joosse, T. P. Philippidis, T Kossivas, G
Fernando
Current Wind
Turbine (WT) Blade certification practices require the
conduction of static and fatigue tests on the blade, in order to
assess whether the blade can sustain the applied loads.
Within the scopes of a
current EC-funded research project, Acoustic Emission (AE)
monitoring has been extensively applied during testing of various WT
Blades of similar design. All blades were loaded to failure by,
either, gradually increasing the static test loads, or fatiguing the
blade until it failed. It has, already, been reported that AE could
well locate the damage imposed on the blade during such tests
(static and fatigue), and in most cases before the damage had become
visible or audible, enhancing, thus, the assessment capabilities and
the understanding of the failure process of the blades.
Additionally, application of typical AE load-and-hold proof tests at
intermediate loading stages prior to failure, has enabled the
assessment of the damage criticality for the particular proof load,
denoted by high acoustic emission rates during load-holds.
Furthermore, it has been observed that the AE behaviour of all
tested blades during load-holds exhibited very similar trends right
prior to failure, despite the fact that blades failed differently.
The present paper
reports on the use of (specially created for the Project) Pattern
Recognition (PR) software which has revealed the existence of a
“critical” class of AE data appearing close to failure. This has
enabled the formulation of specific criteria resulting used for the
automated assessment of the blade’s integrity, based on the amount
of critical hits appearing during the hold period. It is shown that,
for similar blades, common grading criteria can be applied
successfully, enabling a fast and effective “grading” (from “good”
to “severely damaged”), and providing very successful warnings of
impending failure. This is particularly important for the case of
fatigue tests which have lasted for months and have produced huge
amounts of AE data. The software and the automated blade evaluation
will be verified with future tests on large, commercial scale
blades.