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Proceedings Paper

Cognitive sensor networks for structure defect monitoring and classification using guided wave signals
Author(s): Yuanwei Jin; Nicholas O'Donoughue; José M. F. Moura; Joel Harley; James H. Garrett; Irving J. Oppenheim; Lucio Soibelman; Yujie Ying; Lin He
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Paper Abstract

This paper develops a framework of a cognitive sensor networks system for structure defect monitoring and classification using guided wave signals. Guided ultrasonic waves that can propagate long distances along civil structures have been widely studied for inspection and detection of structure damage. Smart ultrasonic sensors arranged as a spatially distributed cognitive sensor networks system can transmit and receive ultrasonic guided waves to interrogate structure defects such as cracks and corrosion. A distinguishing characteristic of the cognitive sensor networks system is that it adaptively probes and learns about the environment, which enables constant optimization in response to its changing understanding of the defect response. In this paper, we develop a sequential multiple hypothesis testing scheme combined with adaptive waveform transmission for defect monitoring and classification. The performance is verified using numerical simulations of guided elastic wave propagation on a pipe model and by Monte Carlo simulations for computing the probability of correct classification.

Paper Details

Date Published: 1 April 2010
PDF: 12 pages
Proc. SPIE 7647, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010, 76473T (1 April 2010); doi: 10.1117/12.848893
Show Author Affiliations
Yuanwei Jin, Univ. of Maryland, Eastern Shore (United States)
Nicholas O'Donoughue, Carnegie Mellon Univ. (United States)
José M. F. Moura, Carnegie Mellon Univ. (United States)
Joel Harley, Carnegie Mellon Univ. (United States)
James H. Garrett, Carnegie Mellon Univ. (United States)
Irving J. Oppenheim, Carnegie Mellon Univ. (United States)
Lucio Soibelman, Carnegie Mellon Univ. (United States)
Yujie Ying, Carnegie Mellon Univ. (United States)
Lin He, Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 7647:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010
Masayoshi Tomizuka, Editor(s)

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