Earlier, an ameliorated MUSIC (Am-MUSIC) algorithm is developed by the authors [1], aimed at expanding conventional MUSIC algorithm from linear array-facilitated nondestructive evaluation to in situ health monitoring with a sparse sensor network. Yet, Am-MUSIC leaves a twofold issue to be improved: i) the signal representation equation is constructed at each pixel across the inspection region, incurring high computational cost; and ii) the algorithm is applicable to monochromatic excitation only, ignoring signal features scattered out of the excitation frequency band which also carry information on structural integrity. With this motivation, a multiple-damage-scattered wavefield model is developed, with which the signal representation equation is constructed in the frequency domain, avoiding computationally expensive pixel-based calculation – referred to as frequency-domain MUSIC (F-MUSIC). F-MUSIC quantifies the orthogonal attributes between the signal subspace and noise subspace inherent in signal representation equation, and generates a full spatial spectrum of the inspected sample to visualize damage. Modeling in the frequency domain endows F-MUSIC with the capacity to fuse rich information scattered in a broad band and therefore enhance imaging precision. Both simulation and experiment are performed to validate F-MUSIC when used for imaging single and multiple sites of damage in an isotropic plate waveguide with a sparse sensor network. Results accentuate that effectiveness of F-MUSIC is not limited by the quantity of damage, and imaging precision is not downgraded due to the use of a highly sparse sensor network – a challenging task for conventional MUSIC algorithm to fulfil.

早些时候，作者 [1] 开发了一种改进的 MUSIC (Am-MUSIC) 算法，旨在将传统的 MUSIC 算法从线性阵列辅助无损评估扩展到原位使用稀疏传感器网络进行健康监测。然而，Am-MUSIC 留下了两个需要改进的问题：i) 在检查区域的每个像素处构建信号表示方程，导致高计算成本；ii) 该算法仅适用于单色激发，忽略散射在激发频带外的信号特征，这些特征也携带结构完整性信息。以此为动机，开发了多损伤散射波场模型，利用该模型在频域中构建信号表示方程，避免了计算成本高昂的基于像素的计算——称为频域 MUSIC（F-音乐）。F-MUSIC 量化信号表示方程中固有的信号子空间和噪声子空间之间的正交属性，并生成被检样本的完整空间谱以可视化损坏。频域建模使 F-MUSIC 能够融合分散在宽带中的丰富信息，从而提高成像精度。当 F-MUSIC 用于对具有稀疏传感器网络的各向同性板波导中的单个和多个损坏部位进行成像时，我们进行了仿真和实验以验证 F-MUSIC。结果强调了 F-MUSIC 的有效性不受损坏数量的限制，并且成像精度不会因为使用高度稀疏的传感器网络而降低——这是传统 MUSIC 算法要完成的一项具有挑战性的任务。