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Proceedings Paper

Sparsity based defect imaging in pipes using guided waves
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Paper Abstract

Pipes are used for the transport of fluids and gases in urban and industrial settings, such as buried pipelines to transport water, oil, and other resources. To ensure reliable operation, it is essential that an inspection system be in place to identify and localize damage/defects in the pipes. Unfortunately, many of the typical nondestructive evaluation techniques are inadequate due to limited pipe access; often, only the beginning and end sections of the pipe are physically accessible. As such, this problem is well suited to the use of ultrasonic guided-wave based structural health monitoring. With a limited number of transducers, ultrasonic guided waves can be used to interrogate long lengths of pipes. In this paper, we propose a damage detection and localization scheme that relies upon the inherent sparsity of defects in the pipes. A sparse array of transducers, deployed in accessible areas of the pipes, is utilized in pitch-catch mode to record signals scattered by defects in the pipe. Both the direct path scattering off the defect, and the helical modes, which are paths that spiral around the circumference of the pipe before or after interaction with the defect, are recorded. A Lamb wave based signal model is formulated that accounts for this multipath approach. The signal model is then inverted via group sparse reconstruction, in order to produce an image of the scene. The model accounts for the specificities of Lamb wave propagation through the pipe. Performance validation of the proposed approach is provided using simulated data for an aluminum pipe.

Paper Details

Date Published: 4 May 2016
PDF: 7 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570K (4 May 2016); doi: 10.1117/12.2223161
Show Author Affiliations
Andrew Golato, Villanova Univ. (United States)
Sridhar Santhanam, Villanova Univ. (United States)
Fauzia Ahmad, Villanova Univ. (United States)
Moeness G. Amin, Villanova Univ. (United States)


Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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