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A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing

机译：用于结构健康监测的贝叶斯优化传感器放置方法，并应用于主动传感

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摘要

This paper introduces a novel approach for optimal sensor and/or actuator placement for structural health monitoring (SHM) applications. Starting from a general formulation of Bayes risk, we derive a global optimality criterion within a detection theory framework. The optimal configuration is then established as the one that minimizes the expected total presence of either type I or type II error during the damage detection process. While the approach is suitable for many sensing/actuation SHM processes, we focus on the example of active sensing using guided ultrasonic waves by implementing an appropriate statistical model of the wave propagation and feature extraction process. This example implements both pulse-echo and pitch-catch actuation schemes and takes into account line-of-site visibility and non-uniform damage probabilities over the monitored structure. The optimization space is searched using a genetic algorithm with a time-varying mutation rate. We provide three actuator/sensor placement test problems and discuss the optimal solutions generated by the algorithm.
机译：本文介绍了一种用于结构健康监测（SHM）应用的最佳传感器和/或执行器位置的新颖方法。从贝叶斯风险的一般公式开始，我们在检测理论框架内得出了全局最优性准则。然后，将最佳配置确定为在损坏检测过程中将I型或II型错误的预期总存在最小化的配置。虽然该方法适用于许多感测/驱动SHM过程，但我们通过实施适当的波传播和特征提取过程统计模型，重点介绍了使用引导超声波的主动感测示例。该示例同时实现了脉冲回波和俯仰捕捉驱动方案，并考虑了现场线的可见性和受监控结构上的非均匀损坏概率。使用具有时变变异率的遗传算法搜索优化空间。我们提供了三个执行器/传感器放置测试问题，并讨论了算法产生的最佳解决方案。

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