High-Rate Structural Health Monitoring and Prognostics: An Overview
High-Rate Structural Health Monitoring and Prognostics: An Overview
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高速结构健康监测和预测：概述
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Erik Blasch
中科院分区：
文献类型：
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作者：
J. Dodson;Austin Downey;S. Laflamme;M. Todd;A. Moura;Yang Wang;Zhu Mao;P. Avitabile;Erik Blasch
结构健康监测既包括静态工程系统，也包括高度动态工程系统。随着实时检测、边缘计算和高带宽计算机内存的出现，实现高速率SHM(HR-SHM)成为可能。本文界定了高速结构健康监测和预测的技术领域，提出了HR-SHM的重大技术挑战，包括：问题的多时间尺度、足够的传感器网络和响应、实时评估以及具有量化的不确定性和风险的决策。在这些挑战中需要解决的关键问题包括事件的持续时间、物理的时间尺度、不确定的多个来源以及硬件执行的有限的时空限制。在包括数据采集、评估执行和决策的综合范式上，本文将高速时间尺度定义为1ms。空间问题包括监测区域的分辨率、通信距离和边缘传感器的数量。时间问题包括传感器类型(例如太赫兹)以及多种不确定因素。这些限制必须结合在一起，以便能够以稳健、适应性强和有益于感兴趣的特派团的高速度执行。为了应对这一巨大挑战，我们提出了高速动态数据的物理知情实时融合(PIRF)。可以进一步利用机器学习和边缘计算等技术来实现对高速率动态系统的结构和功能预测。量化不确定性，包括不确定性和认知性，对于将实时状态估计与置信度联系起来，将风险纳入决策是必要的。
Structural Health Monitoring (SHM) includes both static and highly dynamic engineering systems. With the advent of real-time sensing, edge-computing, and high-bandwidth computer memory, there is an ability to enable high-rate SHM (HR-SHM). The paper defines the technical area of high-rate structural health monitoring and prognostics and presents the HR-SHM technical grand challenges including: multi timescales of the problem, adequate sensor network and response, real-time assessment, and decisionmaking with quantified uncertainty and risk. Key issues to address in such challenges include the time duration of the event, time scales of the physics, multiple sources of uncertainty, as well as limited spatiotemporal constraints for hardware execution. The paper defines the high-rate time scale as 1 ms on the integrated paradigm including data acquisition, assessment execution, and decision-making. The spatial issues include the resolution of the area monitored, the communication distance, and the number of edge sensors. The temporal issue includes the sensor type (e.g., THz) as well as multiple sources of uncertainty. These constraints must be coupled to allow for high-rate implementation that is robust, adaptable, and beneficial to the missions of interest. To address the grand challenge, we propose physicsinformed real-time fusion (PIRF) of high-speed dynamic data. Technologies such as machine learning and edge-computing can be further harnessed to enable structural and functional prognostics for high-rate dynamic systems. Quantification of uncertainty, both aleatory and epistemic, is necessary for real-time state estimation to be connected with the confidences to integrate risks into the decision-making.
影响因子：
8.4
作者：
Downey, Austin;Hong, Jonathan;Scheppegrell, James
通讯作者：
Scheppegrell, James