Neural network AE source location apart from structure size and material

AE localization procedures using artificial neural networks (ANN) represent extremely effective alternative to classical triangulation methods. Nevertheless, their application always requires full-scale, time consuming ANN training on each specific structure. Disadvantage of particularly trained ANN...

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Bibliographic Details
Published in:Journal of acoustic emission 2010-01, Vol.28, p.99
Main Authors: Chlada, Milan, Prevorovsky, Zdenek, Blahacek, Michal
Format: Article
Language:English
Subjects:
Acoustic emission testing
Algorithms
Methods
Neural networks
Online Access:Get full text
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Summary:AE localization procedures using artificial neural networks (ANN) represent extremely effective alternative to classical triangulation methods. Nevertheless, their application always requires full-scale, time consuming ANN training on each specific structure. Disadvantage of particularly trained ANN algorithm is in its non-transferability to any other object. A new ANN-based AE source location approach is proposed in this paper to overcome such limitation. The method replaces standard arrival time differences at the ANN inputs by so called signal arrival time profiles, independent on material and scale changes. The ANN training can be also performed theoretically on geometrical models (i.e. without any experimental errors) and learned ANN is then applied on real structures of different dimensions and materials. Such approach enables considerable extension of ANN application possibilities. The use of new AE source location method is illustrated on experimental data obtained during aircraft structure part testing. Keywords: AE source location, artificial neural networks, arrival time profiles.
ISSN:0730-0050