Informative Bayesian Tools for Damage Localisation by Decomposition of Lamb Wave Signals

TL;DR

This paper introduces a Bayesian signal decomposition method for Lamb wave-based damage localisation in structures, effectively handling complex materials and providing uncertainty quantification, leading to accurate damage position estimates.

Contribution

The paper presents a novel Bayesian approach for decomposing Lamb wave signals, enabling damage localisation with uncertainty quantification in complex structures.

Findings

Bayesian decomposition accurately separates nominal and reflected waves.

Parametric features correlate with physical wave properties.

Damage localisation estimates are within 1mm accuracy.

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. It is useful to decompose the measured signal into the expected waves propagating directly from the actuation source in the absence of damage, and for this paper referred to as nominal waves. This decomposition allows for determination of waves reflected from 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, which has the advantage of quantification of the uncertainty of the expected signal. Furthermore, the approach produces inherent parametric features which correlate to known physics of guided waves. 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 Bayesian decomposition was found to work well for the assessment criteria mentioned above. The use of these waves in the localisation method returned estimates accurate to within 1mm in many sensor configurations.