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.