Ultrasonic flaw detection using radial basis function networks (RBFNs)

Gil-Pita, Roberto; Vicen, Raul; Rosa, Michele De; Jarabo, M.P; Vera, Pedro Sánchez; Curpian, J

doi:10.1016/j.ultras.2003.11.018

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Defective Areas Identification in Aircraft Components by Bivariate EMD Analysis of Ultrasound Signals

Leo

Looney

D’Orazio

et al. 2010

Artificial Neural Networks in Pattern Recognition

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In recent years many alternative methodologies and techniques have been proposed to perform non-destructive inspection and maintenance operations of moving structures. In particular, ultrasonic techniques have shown to be very promising for automatic inspection systems. From the literature, it is evident that the neural paradigms are considered, by now, the best choice to automatically classify ultrasound data. At the same time the most appropriate pre-processing technique is still undecided. The aim of this paper is to propose a new and innovative data pre-processing technique that allows the analysis of the ultrasonic data by a complex extension of the Empirical Mode Decomposition (EMD). Experimental tests aiming to detect defective areas in aircraft components are reported and a comparison with classical approaches based on data normalization or wavelet decomposition is also provided.

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