Informative Bayesian tools for damage localisation by decomposition of Lamb wave signals. (29th September 2022)

• Record Type: Journal Article
• Authors: Haywood-Alexander, Marcus; Dervilis, Nikolaos; Worden, Keith; Dobie, Gordon; Rogers, Timothy J.
• Is Part Of: Journal of sound and vibration. Volume 535(2022)
• Publication Date: 2022-09-29
• Subjects: Guided wave -- Lamb wave -- Damage detection -- Mode decomposition -- Localisation; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2022.117063
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation, thanks to some distinct advantages. Guided waves, in particular Lamb waves, can be used to localise damage by utilising prior knowledge of propagation and reflection characteristics. Typical localisation methods make use of the time of arrival of waves emitted or reflected from the damage, the simplest of which involves triangulation (with a known wave speed). In order to obtain reflection information, it is useful to decompose the measured signal into the expected waves propagating directly from the actuation source in the absence of damage, called a baseline, and for this paper referred to as nominal waves . This decomposition allows for determination of the residual signal which contains only waves from reflection sources such as damage, boundaries or other local inhomogeneities. Previous decomposition methods make use of accurate analytical models, but there is a gap in methods of decomposition for complex materials and structures. A new method is shown here which uses a Bayesian approach to decompose single-source signals, requiring only prior information on surface displacement along the propagation path. This Bayesian decomposition has the advantage of generating a distribution of possible nominal signals and allows for quantification of the uncertainty of the expected signal. Furthermore, the approach produces inherent parametric features which correlate to known physics of guided waves, and likelihood estimates can be used to assess the quality of the decomposition. In this paper, the decomposition method is demonstrated on data from a simulation of guided wave propagation in a small aluminium plate, using the local interaction simulation approach, for a damaged and undamaged case. Analysis of the decomposition method is done in three ways; inspect individual decomposed signals, track the inherently produced parametric features along propagation distance, and use method in a localisation strategy. The localisation method is demonstrated using the decomposed signal at several sensor locations and triangulates for the source of reflected waves from damage. The Bayesian decomposition was found to work well in returning signals containing only reflected waves, as well as obtaining parametric features that can be used to assess damage and confidence in the decomposed wave. The use of these waves in the localisation method returned estimates accurate to within 1 mm in many sensor configurations. Leading on from the work shown here, the paper finishes with future work; the authors intend to extend this method to scenarios where less prior knowledge is available.