Structural health monitoring of impact location and severity using Lamb waves has been proven to be a reliable method under laboratory conditions. However, real-life operational and environmental conditions (vibration noise, temperature changes, different impact scenarios, etc.) and measurement errors are known to generate variation in Lamb wave features which may significantly affect the accuracy of these estimates. Therefore, these uncertainties should be considered, as a deterministic approach may lead to erroneous decisions. In this article, a novel data-driven stochastic Kriging-based method for impact location and maximum force estimation, that is able to reliably quantify the output uncertainty is presented. The method utilises a novel modification of the kriging technique (normally used for spatial interpolation of geostatistical data) for statistical pattern matching and uncertainty quantification using Lamb wave features to estimate the location and maximum force of impacts. The data was experimentally obtained from a composite panel equipped with piezoelectric sensors. Comparison with a deterministic benchmark method developed in prior studies shows that the proposed method gives a more reliable estimate for experimental impacts under various simulated environmental and operational conditions by estimating the uncertainty. The developed method highlights the suitability of data-driven methods for uncertainty quantification, by taking advantage of the relationship between data points in the reference database that is a mandatory component of these methods (and is often seen as a disadvantage). By quantifying the uncertainty, there is more information for operators to reliably locate impacts and estimate the severity, leading to robust maintenance decisions.

使用兰姆波对撞击位置和严重程度进行结构健康监测已被证明是实验室条件下的可靠方法。然而，现实生活中的操作和环境条件（振动噪声、温度变化、不同的冲击场景等）和测量误差已知会产生兰姆波特征的变化，这可能会显着影响这些估计的准确性。因此，应考虑这些不确定性，因为确定性方法可能会导致错误决策。在本文中，提出了一种新颖的基于数据驱动的随机克里金方法，用于冲击定位和最大力估计，能够可靠地量化输出不确定性。该方法利用克里金技术（通常用于地质统计数据的空间插值）的新颖修改，使用兰姆波特征进行统计模式匹配和不确定性量化，以估计撞击的位置和最大力。这些数据是从配备有压电传感器的复合面板中通过实验获得的。与先前研究中开发的确定性基准方法的比较表明，所提出的方法通过估计不确定性，为各种模拟环境和操作条件下的实验影响提供了更可靠的估计。开发的方法突出了数据驱动方法对不确定性量化的适用性，通过利用参考数据库中数据点之间的关系，这是这些方法的强制性组成部分（通常被视为缺点）。通过量化不确定性，运营商可以获得更多信息来可靠地定位影响并估计严重程度，从而做出稳健的维护决策。