Evolutionary dimensionality reduction for crack localization in ship structures using a hybrid computational intelligent approach

Vassilis Kappatos, George Georgoulas, Chrysostomos D. Stylios, Evangelos S. Dermatas

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Abstrakt

Acoustic Emission (AE) is one of the most important Non-Destructive Testing (NDT) methods for materials and constructions. Using AE testing, the location of a single event (crack) can be classified efficiently into three typical areas in a ship hull. The problem is a typical classification problem based on the use of features extracted from piezo-sensors' signal. As in most classification problems, the extraction and selection of the most appropriate set of features plays a major role in the overall performance of the system. In this research work we investigate the use of an evolutionary algorithm to extract new features from a set of primitive features in a lower dimensional space through a linear transformation. These features are subsequently fed into a Probabilistic Neural Network (PNN) that performs the classification. In simulation experiments, where a Stiffened Plate Model (SPM) is partially sank into water, the localization rate in noisy environments outperforms a recent work, where a feature selection phase alone was used before the classification phase. The proposed hybrid computational intelligent approach shows the potential merit of using it in real life situations where the signal is distorted by noise.

OriginalsprogEngelsk
TitelProceedings of the International Joint Conference on Neural Networks
Antal sider8
Publikationsdato2009
Sider1531-1538
Artikelnummer5178852
ISBN (Trykt)9781424435531
DOI
StatusUdgivet - 2009
Udgivet eksterntJa
Begivenhed2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, USA
Varighed: 14. jun. 200919. jun. 2009

Konference

Konference2009 International Joint Conference on Neural Networks, IJCNN 2009
LandUSA
ByAtlanta, GA
Periode14/06/200919/06/2009
SponsorInternational Neural Network Society, IEEE Computational Intelligence Society

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Citationsformater

Kappatos, V., Georgoulas, G., Stylios, C. D., & Dermatas, E. S. (2009). Evolutionary dimensionality reduction for crack localization in ship structures using a hybrid computational intelligent approach. I Proceedings of the International Joint Conference on Neural Networks (s. 1531-1538). [5178852] https://doi.org/10.1109/IJCNN.2009.5178852