论文标题
不确定性意识到多态定位的深度神经网络，并应用于超声波结构健康监测
Uncertainty Aware Deep Neural Network for Multistatic Localization with Application to Ultrasonic Structural Health Monitoring
论文作者
论文摘要
引导的超声波定位使用空间分布的多主传感器阵列和广义波束形成策略来检测和定位结构跨结构的损害。传播通道通常非常复杂。方法可以将数据与波传播模型进行比较以定位损坏。然而，环境不确定性（例如温度或应力变化）通常会降解精度。本文使用不确定性感知的深神经网络框架来学习强大的本地化模型并表示不确定性。我们使用混合密度网络来基于训练数据不确定性生成损伤位置分布。这与大多数本地化方法相反，该方法的输出点估计值。我们将方法与匹配的现场处理（MFP）进行比较，这是一个广义波束成型框架。当数据具有环境不确定性和噪声时，所提出的方法的定位误差为0.0625 m，而使用MFP为0.1425 m。我们还表明，随着环境不确定性的增加，预测不确定性尺度可提供统计意义的度量，以评估本地化准确性。
Guided ultrasonic wave localization uses spatially distributed multistatic sensor arrays and generalized beamforming strategies to detect and locate damage across a structure. The propagation channel is often very complex. Methods can compare data with models of wave propagation to locate damage. Yet, environmental uncertainty (e.g., temperature or stress variations) often degrade accuracies. This paper uses an uncertainty-aware deep neural network framework to learn robust localization models and represent uncertainty. We use mixture density networks to generate damage location distributions based on training data uncertainty. This is in contrast with most localization methods, which output point estimates. We compare our approach with matched field processing (MFP), a generalized beamforming framework. The proposed approach achieves a localization error of 0.0625 m as compared to 0.1425 m with MFP when data has environmental uncertainty and noise. We also show that the predictive uncertainty scales as environmental uncertainty increases to provide a statistically meaningful metric for assessing localization accuracy.
