Scientific Results

Identification of Defective Areas in Composite Materials by Bivariate EMD Analysis of Ultrasound

Year: 2012

Authors: Leo M., Looney D., D’Orazio T., Mandic D.P.

Autors Affiliation: Institute of Intelligent Systems for Automation, Italian National Research Council, 70126 Bari, Italy Department of Electrical and Electronic Engineering, The Imperial College of Science, Technology and Medicine, SW7 2BT London, U.K.

Abstract: In recent years, many alternative methodologies and techniques have been proposed to perform nondestructive 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 preprocessing technique is still undecided. The aim of this paper is to propose a new and innovative data preprocessing technique that converts real-valued ultrasonic data into complex-valued signals. This allows analysis using phase synchrony, a robust tool that has been previously employed in brain science for establishing robust features in noisy data. Synchrony estimation is achieved using complex extensions of empirical mode decomposition, a data-driven algorithm for detecting temporal scales, thus facilitating the modeling of nonlinear and nonstationary signal dynamics. Experimental tests aiming to detect defective areas in composite materials are reported, and the effectiveness of the proposed methodology is illustrated.

Journal/Review: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

Volume: 61 (1)      Pages from: 221  to: 232

KeyWords: Security; BEMD; Neural Networks
DOI: 10.1109/TIM.2011.2150630

Citations: 25
data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2022-01-16
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