Decomposition of multi-mode signals using dispersion curves and Bayesian linear regression

Haywood-Alexander, Marcus and Dervilis, Nikolaos and Worden, Keith and Dobie, Gordon B. and Rogers, Timothy J.; Fromme, Paul and Su, Zhongqing, eds. (2021) Decomposition of multi-mode signals using dispersion curves and Bayesian linear regression. In: Health Monitoring of Structural and Biological Systems XV. Proceedings of SPIE - The International Society for Optical Engineering, 11593 . SPIE, USA. ISBN 9781510640153

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    For certain structure types and damage sizes, guided waves offer some distinct advantages for damage detection, such as range and sizing potential, greater sensitivity and cost effectiveness. Guided waves exhibit multiple modes; for Lamb waves there are two types; symmetric and antisymmetric. In damage detection regimes, information and features of individual modes, which propagate from a single source, are useful for localisation and sizing of damage. This facet leads to the motivation to decompose a single signal into the individual modes that are received in the wave-packet. Decomposition of wave modes is possible in full-field Lamb wave data from a forward-backward, two-dimensional Fourier transform method that involves dispersion curve information; though this method cannot be applied directly to signals at a single location. By using this method, the expected nominal waves can be determined for a given propagation distance; i.e. the individual wave modes expected to be present regardless of damage. In the presence of damage, residual signals will be present which contain information on the damage. In this paper, a Bayesian linear regression technique is used to decompose single multi-mode signals into their individual wave modes, which is then used to determine any residual signals. This decomposition is made by determining the expected shape and size of individual mode signals from the full-field decomposed waves. The information inferred by this method, both before and after the wave has propagated through damage, is studied.

    ORCID iDs

    Haywood-Alexander, Marcus, Dervilis, Nikolaos, Worden, Keith, Dobie, Gordon B. ORCID logoORCID: and Rogers, Timothy J.; Fromme, Paul and Su, Zhongqing