Identification of Anomaly in Structures Based on Locally Controlled Dynamic Inputs

基于局部控制动态输入的结构异常识别

• 批准号: 0424141
• 负责人: Fu-Kuo Chang
• 金额: $ 29万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0424141&HistoricalAwards=false
• 关键词: Identification; Anomaly; Structures; Based; Locally

AbstractCMS-0424141Chang, Fu-KuoStanford UniversityRapid and accurate detection of anomaly in structures while in service is major a challenge in engineering. Recent advances in sensor and smart materials technologies provide promising opportunities to overcome current time-consuming and labor-intensive inspection methods. The key to the success of the sensor-based technologies depends strongly on how the sensor measurements can be correlated with the physical quantity in terms of size and location of the anomaly. Although challenging, the mathematical complexity of the sensor-based systems becomes significantly reduced if the inputs to generate the data are well controlled. This leads to a fundamental mathematical issue: Given limited sensor data resulting from controlled inputs, identify local condition of an object.Therefore, an investigation is undertaken to develop a mathematical framework for detecting anomaly in structures using distributed sensor measurements generated from locally controlled dynamic excitations as well as appropriate algorithms to identify the location and size of the anomaly based on the measurements. Both analytical and experimental work will be conducted during the investigation. The major tasks to be performed for the study include: Diagnostic Signal Generation, Signal Interrogation and Interpretation, and Implementation and Verification. Although simple coupon tests will be used to verify the results, the mathematical framework is fundamental and shall allow engineers to explore new mathematical formulations for data interpretation of any complex systems. For instance, the framework could be applied to monitor fatigue cracks in aircraft structures, to detect corrosion cracks for underground pipelines, to provide early warning of incipient failure in spacecraft, or to interrogate the integrity of bridges or buildings after major quakes. Furthermore, the interrogation algorithms will provide useful tools for readily applying sensing and monitoring techniques for a broad range of engineering fields.This project is supported by CMS under the Math-Eng Interfacing Initiative.

快速准确地检测结构在使用中的异常是工程上的一个主要挑战。 传感器和智能材料技术的最新进展为克服当前耗时和劳动密集型的检测方法提供了有希望的机会。基于传感器的技术成功的关键在很大程度上取决于传感器测量如何与异常的大小和位置方面的物理量相关联。虽然具有挑战性，但如果生成数据的输入得到很好的控制，基于传感器的系统的数学复杂性将显著降低。这就引出了一个基本的数学问题：给定有限的传感器数据，从控制输入，识别局部条件的object.So，调查进行开发一个数学框架，用于检测结构中的异常使用分布式传感器测量产生的本地控制动态激励以及适当的算法，以确定位置和大小的异常的测量的基础上。调查期间将进行分析和实验工作。 本研究的主要任务包括：诊断信号生成、信号询问和解释以及实施和验证。虽然简单的试样测试将用于验证结果，数学框架是基本的，并应允许工程师探索新的数学公式的数据解释的任何复杂的系统。 例如，该框架可用于监测飞机结构的疲劳裂纹，检测地下管道的腐蚀裂纹，提供航天器早期故障的早期预警，或在大地震后询问桥梁或建筑物的完整性。 此外，询问算法将提供有用的工具，方便地将传感和监测技术应用于广泛的工程领域。