Structural Health Monitoring (SHM) aims to monitor in real time the health state of engineering structures. For thin structures, Lamb Waves (LW) are very efficient for SHM purposes. A bonded piezoelectric transducer (PZT) emits LW in the structure in the form of a short tone burst. This initial wave packet (IWP) propagates in the structure and interacts with its boundaries and discontinuities and with eventual damages generating additional wave packets. The main issues with LW based SHM are that at least two LW modes are simultaneously excited and that those modes are dispersive. Matching Pursuit Method (MPM), which consists of approximating a signal as a sum of different delayed and scaled atoms taken from an a priori known learning dictionary, seems very appealing in such a context, however is limited to nondispersive signals and relies on a priori known dictionary. An improved version of MPM called the Single Atom Convolutional Matching Pursuit method (SACMPM), which addresses the dispersion phenomena by decomposing a measured signal as delayed and dispersed atoms and limits the learning dictionary to only one atom, is proposed here. Its performances are illustrated when dealing with numerical and experimental signals as well as its usage for damage detection. Although the signal approximation method proposed in this paper finds an original application in the context of SHM, this method remains completely general and can be easily applied to any signal processing problem.

翻译：结构健康监测（SHM）旨在实时监测工程结构的健康状态。对于薄壁结构，兰姆波（LW）在SHM应用中非常高效。一个粘贴式压电换能器（PZT）以短脉冲串的形式在结构中激发兰姆波。这个初始波包（IWP）在结构中传播，与其边界、不连续性以及可能的损伤相互作用，产生额外的波包。基于兰姆波的SHM面临的主要问题在于，至少有两种兰姆波模式被同时激发，且这些模式具有频散特性。匹配追踪方法（MPM）通过将信号近似为从先验已知的学习字典中选取的不同延迟和缩放原子的叠加，在此类情境下显得极具吸引力，但该方法仅限于非频散信号，且依赖于先验已知的字典。本文提出了一种改进的MPM版本，称为单原子卷积匹配追踪方法（SACMPM）。该方法通过将测量信号分解为延迟且频散的原子来处理频散现象，并将学习字典限制为仅包含一个原子。文中通过处理数值和实验信号，并展示其在损伤检测中的应用，验证了该方法的性能。尽管本文提出的信号近似方法在SHM领域找到了创新性应用，但该方法具有完全的通用性，可轻松应用于任何信号处理问题。