CPS: Medium: Collaborative Research: Scalable Intelligent Backscatter-Based RF Sensor Network for Self-Diagnosis of Structures

CPS：中：协作研究：用于结构自诊断的可扩展智能反向散射射频传感器网络

基本信息

批准号：
2038761
负责人：
Branko Glisic
金额：
$ 40万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038761&HistoricalAwards=false
关键词：
CPS Medium Collaborative Research Scalable

项目摘要

This Cyber-Physical Systems (CPS) grant will advance structural health monitoring of concrete structures by relying on data acquired by a novel sensing technology with unprecedented scalability and spatial resolution. Modern society depends critically on sound and steadfast functioning of a variety of engineering structures and infrastructures, such as bridges, buildings, pipelines, geotechnical structures, aircrafts, wind turbines, and industrial facilities. Due to aging, massive urbanization, and climate change, there is a growing need for accurate and reliable assessment of the health condition, performance, and operation of these structures in order to ensure their continuous functioning and safe use. The researched technology enables pervasive and scalable sensing of concrete structures with high resolution by transforming concrete into a smart self-sensing material, thereby enabling reliable long-term structural health monitoring. This in turn contributes to the nation’s sustainability and resilience and to advancing the nation’s prosperity, welfare, and security. The project advances multiple core research areas in structural health monitoring including CPS system architectures using embedded devices, multi-parameter sensing and networking based on radio frequency sensors, and machine learning for accurate and reliable data analytics. The research outcomes are highly translational to various other CPS domains. The project also contributes to secondary education and outreach activities in multiple ways as well as to undergraduate and graduate education. The aim of this project is to create a novel sensing system comprised of radio frequency sensors that are pervasively embedded in large volumes of concrete structures and that sense their localities using radio frequency properties. The objective is the assessment of key parameters that reflect the behavior of the monitored structure under operational conditions, such as deformation, temperature, and humidity, as well as detection and characterization of damages. The project has the following intellectual contributions: 1) Passive radio frequency-based sensing that operates over a wide range of frequencies; architectures of smart exciters and networked radio frequency sensors that communicate among themselves via backscatter modulation; solar-powered radio frequency exciter platform that powers the sensors. 2) Energy-based sensing and network optimization of the radio frequency sensor network in terms of its monitoring ability and network connectivity given the constraints on the available harvested power at the exciters. 3) Machine learning methods for function estimation based on the principle of ensemble modeling with Gaussian processes and applied to self-localization and to inference of three-dimensional distributions of material parameters within large volumes of concrete structures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项网络物理系统 (CPS) 拨款将依靠具有前所未有的可扩展性和空间分辨率的新型传感技术获取的数据来推进混凝土结构的结构健康监测。由于老化、城市化和气候变化，越来越需要对桥梁、建筑物、管道、岩土结构、飞机、风力涡轮机和工业设施等基础设施的健康状况、性能和性能进行准确且高度可靠的评估。和因此，所研究的技术通过将混凝土转变为智能自感知材料，能够以高分辨率对混凝土结构进行普遍且可扩展的传感，从而实现可靠的长期结构健康监测。这反过来又有助于国家的可持续性和复原力，并促进国家的繁荣、福利和安全。该项目推进了结构健康监测的多个核心研究领域，包括使用嵌入式设备、多参数传感和基于无线电的网络的 CPS 系统架构。频率传感器，该项目还以多种方式为中等教育和推广活动以及本科生和研究生教育做出了贡献。创建一种由射频传感器组成的新型传感系统，该系统普遍嵌入大量混凝土结构中，并利用射频特性来感知其位置，其目标是评估反映被监测结构在运行条件下行为的关键参数。，如变形、温度和湿度，该项目具有以下智力贡献：1）在广泛的频率范围内运行的基于无源射频的传感；通过反向散射调制相互通信的智能激励器和网络射频传感器的架构。 ; 为传感器供电的太阳能射频激励器平台 2) 考虑到激励器可获取功率的限制，射频传感器网络在其监测能力和网络连接方面进行基于能量的传感和网络优化。）基于高斯过程系综建模原理的函数估计机器学习方法，应用于自定位和推断大量混凝土结构中材料参数的三维分布。该奖项反映了 NSF 的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。