Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, MH, India.
Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, MH, India.
Ultrasonics. 2021 Aug;115:106439. doi: 10.1016/j.ultras.2021.106439. Epub 2021 Apr 16.
Compressive sensing (CS) has been widely explored for data compression and signal recovery in presence of lossy transmission in structural health monitoring (SHM) applications. Discussions of lost data recovery using CS reported in literature are typically limited to acceleration signals obtained from vibration based SHM systems. Moreover these reports limit the study to performance analysis of recovery of signals in time domain, while feasibility of these algorithm on subsequent damage analysis using recovered signals remains unexplored. A systematic evaluation of performance of CS based signal recovery for algorithmic estimation of damage index (DI) in ultrasound SHM systems is important for determining their practicality for automated SHM applications. In this paper, we study the feasibility of DI estimation in ultrasonic guided wave testing of honeycomb composite sandwich structures (HCSS) using signals recovered from lossy sensor recordings. We emulate signal loss by masking the sensor recordings in an experimentally measured dataset comprising of an HCSS panel with two defects (disbond and high density (HD) core) instrumented with eight piezoelectric wafer and employ orthogonal matching pursuit (OMP) based signal recovery algorithm. Our analysis suggests that while OMP-based signal recovery algorithm is a robust and reliable signal recovery technique, producing signal reconstruction errors lesser than 8.4% for data loss as high as 50%, the magnitude error in DI estimation is significant and varies for different signal difference coefficient (SDC) algorithms. We propose alternate SDC definition, SDC, computed using peak amplitude of the Hilbert transform (HT), that shows consistently less error than the conventional cumulative-sum-based SDC definition for the HCSS case study. Further we study trends of error in recovery of lossy time domain signals as well as DI computation as a function of data loss parameters, for both random as well as continuous data loss. Our findings indicate that conventional DI computation algorithms for ultrasonic SHM need to be revisited when used in compressive sensing paradigm.
压缩感知 (CS) 在结构健康监测 (SHM) 应用中，在有损传输的情况下，已被广泛用于数据压缩和信号恢复。文献中报道的使用 CS 进行丢失数据恢复的讨论通常仅限于从基于振动的 SHM 系统获得的加速度信号。此外，这些报告将研究限制在时域中信号恢复性能的分析上，而使用恢复的信号进行后续损伤分析的这些算法的可行性仍未得到探索。对基于 CS 的信号恢复算法在超声 SHM 系统中损伤指标 (DI) 估计中的性能进行系统评估，对于确定它们在自动化 SHM 应用中的实用性非常重要。在本文中，我们研究了使用从有损传感器记录中恢复的信号来估计超声导波测试中蜂窝复合材料夹层结构 (HCSS) 的 DI 的可行性。我们通过在一个实验测量数据集上对传感器记录进行掩蔽来模拟信号丢失，该数据集包含一个带有两个缺陷（脱粘和高密度 (HD) 芯）的 HCSS 面板，并用八个压电晶片进行了仪器化，并采用基于正交匹配追踪 (OMP) 的信号恢复算法。我们的分析表明，虽然 OMP 基于的信号恢复算法是一种稳健可靠的信号恢复技术，对于高达 50%的数据丢失，信号重建误差小于 8.4%，但 DI 估计的幅度误差很大，并且因不同的信号差分系数 (SDC) 算法而异。我们提出了替代的 SDC 定义，即 SDC，它使用希尔伯特变换 (HT) 的峰值幅度计算，对于 HCSS 案例研究，它比传统的基于累积和的 SDC 定义显示出更小的误差。进一步，我们研究了在有损时域信号的恢复以及 DI 计算中，作为数据丢失参数的函数的误差趋势，对于随机和连续数据丢失都进行了研究。我们的发现表明，在压缩感知范式中使用时，需要重新审视传统的超声 SHM 中的 DI 计算算法。
