Informative Bayesian tools for damage localisation by decomposition of Lamb wave signals
Informative Bayesian tools for damage localisation by decomposition of Lamb wave signals
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DOI：
10.1016/j.jsv.2022.117063
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发表时间：
2022-07-06
影响因子：
4.7
通讯作者：
Rogers, Timothy J.
中科院分区：
文献类型：
--
作者：
Haywood-Alexander, Marcus;Dervilis, Nikolaos;Rogers, Timothy J.
超声导波技术以其独特的优点为结构健康监测和无损评价提供了一种方便实用的方法。导波，特别是兰姆波，可以用于通过利用传播和反射特性的先验知识来定位损坏。典型的定位方法利用从损坏处发射或反射的波的到达时间，其中最简单的方法涉及三角测量（具有已知的波速）。为了获得反射信息，有用的是将测量信号分解成在没有损坏的情况下直接从激励源传播的预期波，称为基线，并且对于本文称为标称波。这种分解允许确定仅包含来自反射源（例如损坏、边界或其他局部不均匀性）的波的残余信号。以前的分解方法利用精确的分析模型，但在复杂材料和结构的分解方法中存在差距。本文提出了一种新的方法，它使用贝叶斯方法分解单源信号，只需要沿传播路径沿着表面位移的先验信息，这种贝叶斯分解具有产生可能的标称信号分布的优点，并允许量化预期信号的不确定性。此外，该方法产生与导波的已知物理学相关的固有参数特征，并且似然估计可以用于评估分解的质量。在本文中，分解方法是从一个小铝板的导波传播模拟的数据上证明，使用局部相互作用模拟的方法，对于损坏和未损坏的情况下。分解方法的分析以三种方式进行;检查各个分解信号，沿着沿着传播距离跟踪固有产生的参数特征，以及在定位策略中使用方法。使用分解的信号在几个传感器的位置和三角测量的源从损坏的反射波的本地化方法进行了演示。贝叶斯分解被发现在返回仅包含反射波的信号以及获得可用于评估分解波中的损伤和置信度的参数特征方面工作良好。在定位方法中使用这些波在许多传感器配置中返回精确到1 mm内的估计。从这里所示的工作开始，本文完成了未来的工作;作者打算将这种方法扩展到先验知识较少的场景。
Ultrasonic guided waves offer a convenient and practical approach to structural health mon-itoring 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.