Due to extensive detection range and high sensitivity to defects, ultrasonic Lamb waves are extensively studied in the fields of Nondestructive Testing and Structural Health Monitoring. In scenarios where the material parameters or geometric parameters of the waveguide are unknown, the dispersion relation of the guided wave cannot be calculated by the forward model. Consequently, it becomes imperative to extract wave propagation characteristics of Lamb wave from the acquired Lamb wave data. This paper presents a multitask complex hierarchical sparse Bayesian learning (MuCHSBL) method which is aimed at enhancing the efficacy of the dispersion relation solution by considering the continuity of the recovered dispersion curve in the frequency-wavenumber domain. Furthermore, the posterior distributions quantified by MuCHSBL are employed to optimize the placement of measurement points. Numerical and experimental studies are conducted to verify the effectiveness of the proposed method. Comparison analysis with the conventional approach demonstrates the significant enhancement in accuracy of recovering dispersion curves by the proposed method.

中文翻译：

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基于多任务复杂分层稀疏贝叶斯学习的抗离群值导波色散曲线恢复与测量布局优化


由于超声波的检测范围广且对缺陷高度敏感，因此在无损检测和结构健康监测领域得到了广泛的研究。在波导的材料参数或几何参数未知的情况下，正向模型无法计算导波的色散关系。因此，必须从采集的 Lamb 波数据中提取 Lamb 波的波传播特性。该文提出了一种多任务复杂分层稀疏贝叶斯学习 （MuCHSBL） 方法，旨在通过考虑恢复的色散曲线在频波数域中的连续性来提高色散关系解的有效性。此外，采用 MuCHSBL 量化的后验分布来优化测量点的放置。通过数值和实验研究验证了所提方法的有效性。与传统方法的比较分析表明，所提出的方法显著提高了恢复色散曲线的准确性。