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Bayesian synthesis for simulation-based generation of probability of detection (PoD) curves

机译:贝叶斯合成用于仿真的检测概率(POD)曲线

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摘要

This paper examines the feasibility of using Bayesian synthesis to reduce the number of experimental cases and trials required for generation of probability of detection (PoD) curves. A Bayesian framework is developed for the data-level combination of experimental and simulated datasets, in the context of the inspection of back-wall breaking notches in metallic samples by bulk ultrasonic shear waves. PoD curves generated using the proposed approach, where results from a reduced number of experimental defect cases and trials are used in combination with simulated datasets, are shown to compare well with those from the conventional approach using a large number of experiments. Finally, the framework is also shown to be versatile for generating PoD curves for complex defects (illustrated through the example of an inclined notch) using simulations for canonical defects (vertical notches). (C) 2017 Elsevier B.V. All rights reserved.
机译:本文研究了使用贝叶斯合成的可行性,以减少产生检测概率(POD)曲线所需的实验情况和试验的数量。 在通过散装超声剪切波检测金属样品中的背壁破碎槽口的背景下,开发了贝叶斯框架的实验和模拟数据集的数据级组合。 使用该方法产生的POD曲线,其中来自减少实验缺陷案例和试验的结果与模拟数据集结合使用,显示与使用大量实验的传统方法的那些进行比较。 最后,框架也被证明是一种多功能,用于使用针对规范缺陷(垂直凹口)的模拟来产生用于复杂缺陷的POD曲线(通过倾斜凹口的示例所示)。 (c)2017 Elsevier B.v.保留所有权利。

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