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Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring

机译：引导波结构健康监测中使用稀疏抽样的缺陷检测异常分析

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摘要

We propose an outlier detection-based statistical approach to identify and locate a defect in composite plates using far fewer number of sensing points compared to conventional imaging techniques. The key steps involved in this computationally inexpensive approach are the random sparse selection of the sensing points through Poisson disk sampling, followed by a two-step outlier detection process based on thresholding and computation of median absolute deviation. The robustness of the proposed technique is explored through extensive simulations involving different defect sizes, random locations on flat plate structures, and various values of signal to noise ratio (SNR). We experimentally demonstrate the feasibility of detection of delamination, whose size is comparable to the ultrasonic wavelength with probability of detection (PoD) better than 90% using % of the total number of samples required for conventional imaging, even under conditions wherein the SNR is as low as 5 dB.
机译：我们提出了一种基于检测的统计方法，与传统成像技术相比，使用较少数量的感测点来识别和定位复合板中的缺陷。在这种计算廉价的方法中涉及的关键步骤是通过泊松磁盘采样对传感点的随机稀疏选择，然后基于阈值和计算中位绝对偏差的计算。通过涉及不同缺陷尺寸，平板结构上的随机位置的广泛模拟来探讨所提出的技术的鲁棒性，以及对噪声比（SNR）的各种信号值。我们通过实验证明了检测分层的可行性，其尺寸与检测概率（POD）的超声波波长相当，使用传统成像所需的样本总数的百分比，即使在SNR为的条件下也是如此低至5 dB。

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