Guided wave time-reversal imaging of macroscopic localized inhomogeneities in anisotropic composites
Publication date
2019
Document type
Research article
Author
Eremin, Artem
Glushkov, Evgeny
Glushkova, Natalia
Lammering, Rolf
Organisational unit
ISSN
Series or journal
Structural Health Monitoring
Periodical volume
18
Periodical issue
5-6
First page
1803
Last page
1819
Peer-reviewed
✅
Part of the university bibliography
✅
Keyword
Anisotropy
Sensor Network
Structural Health Monitoring
Abstract
Estimation of damage position and extent in the inspected structure is among the emerging problems of active structural health monitoring (SHM) with elastic guided waves. Unlike conventional non-destructive testing (NDT) techniques, which assume continuous surface scanning, SHM systems operate only with data from a limited number of sensors. Nevertheless, in the case of isotropic (metal) plate-like structures, computational time-reversal techniques have proved to be effective for estimating in situ the location and size of wave sources and/or local scatterers within the SHM concept. With composite plates, the reconstruction procedure faces additional difficulties associated with the complexity of wave phenomena caused by their lamination and anisotropy. In this article, we present an extension of the time reversal technique for the case of composite laminate plate-like structures. This technique relies on the simulation of the reversed guided waves generated by reciprocal surface loads applied at a sparse set of measurement points of a real sensor network. The proposed implementation is based on the far-field asymptotics for guided waves generated in arbitrarily anisotropic laminate waveguides by a prescribed source, which have been derived from the path integral representations in terms of Green’s matrix for the structures considered. The performance of this approach has been experimentally tested on cross-ply carbon fiber-reinforced plastic plates showing reliable and adequate results for both active (piezoactuators) and passive (artificial defects) source characterization. © The Author(s) 2019.
Cite as
Enthalten in: Structural health monitoring. - Thousand Oaks, Calif. : Sage Publications, 2002. - Online-Ressource. - Bd. 18.2019, insges. 17 S.
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