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 <1% of the total number of samples required for conventional imaging, even under conditions wherein the SNR is as low as 5 dB.

中文翻译：

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稀疏采样的异常值检测在导波结构健康监测中的异常值分析

与传统的成像技术相比，我们提出了一种基于异常值检测的统计方法来识别和定位复合板中的缺陷，使用的传感点数量要少得多。这种计算上便宜的方法涉及的关键步骤是通过泊松磁盘采样随机稀疏选择感测点，然后是基于阈值和中值绝对偏差的两步离群值检测过程。通过广泛的仿真，包括不同的缺陷尺寸，平板结构上的随机位置以及各种信噪比（SNR）值，探索了所提出技术的鲁棒性。我们通过实验证明了分层检测的可行性，分层的大小可与超声波波长相媲美，且检测概率（PoD）优于90％使用<1 ％为常规成像所需的样本的总数，即使条件下，其中所述SNR是低至5分贝。