Lamb wave techniques have been widely used for structural health monitoring (SHM) and nondestructive testing (NDT). To deal with dispersive and multimodal problems of Lamb wave signals, many signal processing methods have been developed. A spatially distributed array of piezoelectric transducers is generally adopted for both transmission and reception of Lamb waves. When imaging the damage in composite laminates, it is necessary to meet the need of processing array signals with high efficiency. In this paper, the multiple sparse Bayesian learning (M-SBL) strategy is employed for damage imaging. Multiple residual signals including damage-reflection waves are decomposed into a sparse matrix of location-based components simultaneously. An appropriate dictionary is designed to match the damage-reflection waves instead of interference waves. The key to success is to obtain the sparse matrix of weighting coefficients through the M-SBL algorithm. Damage imaging can be achieved efficiently using the delay-and-sum (DAS) method with sparse coefficients in time-domain. Results from the experiment in composite laminates demonstrate the effectiveness of the proposed method.

兰姆波技术已广泛用于结构健康监测（SHM）和无损检测（NDT）。为了处理兰姆波信号的色散和多峰问题，已经开发了许多信号处理方法。通常将压电换能器的空间分布阵列用于兰姆波的发送和接收。当对复合层压板中的损坏进行成像时，有必要满足高效处理阵列信号的需求。本文采用多重稀疏贝叶斯学习（M-SBL）策略进行损伤成像。包括损伤反射波在内的多个残留信号会同时分解为基于位置的组件的稀疏矩阵。设计适当的字典以匹配损伤反射波而不是干扰波。成功的关键是通过M-SBL算法获得加权系数的稀疏矩阵。使用时域稀疏系数的延迟和（DAS）方法可以有效地实现损伤成像。复合材料层压板的实验结果证明了该方法的有效性。