Data Fusion of Heterogeneous Sensor Measurements for Enhanced Structural Modeling

用于增强结构建模的异构传感器测量数据融合

• 批准号: 1100321
• 负责人: Andrew Smyth
• 金额: $ 28.07万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1100321&HistoricalAwards=false
• 关键词: Data; Fusion; Heterogeneous; Sensor; Measurements

The objective of this project is to effectively combine the qualities of different sensor types of a dynamic monitoring network to capitalize on the intrinsic redundancies of the measured data to identify the structural model parameters. Currently there is increasing activity in the area of structural health monitoring using newly emerging, dynamic sensor technologies. There is, however, no clear framework to best combine these heterogeneous measurement quantities for health monitoring purposes. In this project, this dual parameter and state estimation problem with different types of sensor measurements is formulated as a nonlinear estimation problem. In this study, the challenges that will be addressed in dealing with this nonlinear dual state and parameter estimation problem are: 1) the implementation of the approach to large structural problems with many unmeasured states and parameters to be identified and 2) determining the required sensor configurations and resolution to ensure "observability" such that the measured quantities are, indeed, useful and usable for this nonlinear estimation problem. The theoretical developments and the proposed identification approach will be experimentally validated with the laboratory model of a building structure and also with a leveraged data set from a major long-span bridge collected by the principal investigator. This study is expected to provide a validated approach to maximize the return on the use of the heterogeneous sensor networks and an important practical tool to the structural engineering community for better health monitoring, management and maintenance of critical civil infrastructure system with improved life safety. The PI has an industry/agency outreach plan, and will rapidly introduce the dual state-parameter estimation concepts in a graduate course under development. The project will also provide advanced training to graduate and undergraduate students through their direct involvements in this project.

该项目的目的是有效地结合动态监控网络的不同传感器类型的质量，以利用测量数据的内在冗余，以识别结构模型参数。当前，使用新出现的动态传感器技术在结构健康监测领域的活动越来越高。但是，没有明确的框架可以最好地将这些异质测量量结合起来，以实现健康监测目的。在该项目中，具有不同类型的传感器测量值的双重参数和状态估计问题被公告为非线性估计问题。在这项研究中，应对这种非线性双重状态和参数估计问题的挑战是：1）实施用于大型结构问题的方法，并确定许多未衡量的状态和参数，以及2）确定所需的传感器配置和解决方案，以确保“观察力”，以使测量量的数量实际上是有用的，并且对不合格的估计是有用的。理论发展和提议的识别方法将通过建筑物结构的实验室模型以及由主要研究者收集的主要长跨桥梁的杠杆数据集来实验验证。预计这项研究将提供一种经过验证的方法，以最大程度地利用异质传感器网络的使用回报率，以及对结构工程社区的重要实用工具，以更好地监测，管理和维护具有改善生命安全的关键民用基础设施系统。 PI有一个行业/代理机构推广计划，并将在正在开发的研究生课程中迅速引入双重国家参数估计概念。该项目还将通过直接参与该项目为研究生和本科生提供高级培训。