Innovative Signal Analysis Techinques for Health Monitoring Systems and Sensing Materials

用于健康监测系统和传感材料的创新信号分析技术

• 批准号: 0140710
• 负责人: Jann Yang
• 金额: $ 26.57万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-01 至 2006-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0140710&HistoricalAwards=false
• 关键词: Innovative; Signal; Analysis; Techinques; Health

One important link in the health monitoring system is the data processing techniques, including the corresponding theory and algorithms, to extract relevant information for a rapid assessment of the state of the structure from a large amount of measured data. Such a link has been pointed out during many conferences as one of the weakest link in the sensor-based health monitoring system of civil infrastructures. This project is aiming at the development of innovative and efficient data processing techniques, based on the Hilbert-Huang spectral analysis, for the damage detection, condition assessment and integrity evaluation of civil infrastructures. New and efficient methodologies will be developed for: (i) damage detection and system identification of linear structures, (ii) determination of the optimal placement of sensors, (iii) damage detection and system identification of nonlinear structures, including conservative (elastic) and nonconservative (hysteretic) nonlinear structures, and (iv) preliminary assessment of structural damages after an extreme event, such as strong earthquakes, all based on the Hilbert-Huang spectral analysis. Further, Hilbert-Huang spectral analysis computer software modules for incorporating into the sensor-based monitoring systems of civil infrastructures will be developed.This proposal is a part of an integrated collaborative research effort among U.S.A., Hong Kong and P.R.C., under the US-China Protocol for Scientific and Technical Cooperative Research on Earthquake Engineering and Hazard Mitigation. Basically, advanced health monitoring system research for civil infrastructure systems will be conducted at Hong Kong Polytechnic University (HPU); advanced sensor technology will be developed at Harbin Institute of Technology (HIT), China; sensing materials for sensors will be investigated at Institute of Engineering Mechanics (IEM), China; advanced and smart damping and devices will be developed at University of Notre Dame (UND); and novel data processing techniques will be developed at the University of California, Irvine (UCI). In addition, Dr. N. E. Huang of NASA Goddard Flight Center, who is the pioneer of the Hilbert-Huang spectral analysis, will collaborate with us in the proposed research efforts to develop novel data processing techniques for sensor-based monitoring systems for civil infrastructures.The research proposed herein is unique, which is different from all the data analysis techniques currently available. The Hilbert-Huang spectral analysis can be used to develop powerful techniques with which intrinsic nonstationary and nonlinear characteristics of the measured response signals can be extracted. The results of this research are expected to have a significant impact not only on the state-of-the-technology in data analyses for the sensor-based health monitoring systems of structures but also on the sensor technologies to be developed by the collaborative research institutions in U.S.A., Hong Kong and China.

健康监测系统中的一个重要环节是数据处理技术，包括相应的理论和算法，以便从大量的测量数据中提取相关信息，以便快速评估结构的状态。在许多会议上指出，这种联系是民用基础设施基于传感器的健康监测系统中最薄弱的环节之一。该项目旨在开发基于希尔伯特-黄谱分析的创新和高效的数据处理技术，用于民用基础设施的损伤检测、状态评估和完整性评估。新的有效方法将被开发用于：(I)线性结构的损伤检测和系统识别，(Ii)确定传感器的最佳位置，(Iii)非线性结构的损伤检测和系统识别，包括保守的(弹性的)和非保守的(滞后的)结构，以及(Iv)在极端事件后的结构损伤的初步评估，例如强震，所有这些都基于Hilbert-Huang谱分析。此外，还将开发希尔伯特-黄频谱分析计算机软件模块，用于纳入基于传感器的民用基础设施监测系统。这项建议是美国、香港和中华人民共和国根据美国-中国地震工程和减灾科学技术合作研究协议开展的综合合作研究工作的一部分。基本上，香港理工大学将进行土木工程基础设施系统的先进健康监测系统研究；中国哈尔滨工业大学将开发先进的传感器技术；中国的工程力学研究所将研究传感器的传感材料；圣母大学将开发先进的智能减震和装置；加州大学欧文分校将开发新的数据处理技术。此外，美国宇航局戈达德飞行中心的黄N.E.博士是希尔伯特-黄频谱分析的先驱，他将与我们合作，在拟议的研究工作中，为基于传感器的民用基础设施监测系统开发新的数据处理技术。希尔伯特-黄谱分析可以用来发展强大的技术，利用它可以提取被测响应信号的内在非平稳和非线性特征。这项研究的结果不仅将对基于传感器的结构健康监测系统的数据分析技术的最新发展产生重大影响，而且将对美国、香港和中国等合作研究机构即将开发的传感器技术产生重大影响。