Handling Noise-Contaminated Data and Nonunique Identification Results in Wireless Sensor Networks for Structural Health Monitoring

处理结构健康监测无线传感器网络中的噪声污染数据和非唯一识别结果

• 批准号: 0332350
• 负责人: Jin-Song Pei
• 金额: $ 3万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2005-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0332350&HistoricalAwards=false
• 关键词: Handling; Noise; Contaminated; Data; Nonunique

PI: Jin-Song Pei, University of Oklahoma This SGER will support exploratory research on wireless sensor networks for structural health monitoring (SHM) with focus on handling noise-contaminated data measurements and nonunique system identification results, the two main issues that have been identified with the most critical and urgent needs in the context of the rapidly growing sensor networks, especially wireless sensor networks. Untested aggressive novel ideas are presented in the proposal while analytical development as well as numerical and experimental validation will be carried out in the project. Therefore, the project is considered as exploratory and high risk. The project research will be undertaken through collaboration between the University of Oklahoma (OU) and Massachusetts Institute of Technology (MIT). The PI at OU will advise a PhD student at MIT to conduct key parts of the research efforts. Experimental study will be conducted at OU with the participation of the PI's team and the student from MIT during each summer while analytical and numerical work will be carried out throughout the two academic years. The main research ideas stem from the PI's successful PhD work at Columbia University on transparent and engineered Artificial Neural Networks (ANNs) with a development plan expanded drastically in both breadth and depth. Serving as a proof-of-concept for the PI's novel ideas on sensor network design and data interpretation, the results will be used to build the PI's academic credentials for her future submission for NSF CAREER grant and participations in future major NSF solicitations such as .Sensors and Sensor Networks.. As an individual member and an institution alternate representative of OU at NEES Consortium, the PI will apply the ideas and methods developed in this project not only to SHM but also to earthquake engineering, especially those NSF NEES related activities. The intellectual merit of the proposed activity includes advancing knowledge on sensor technology and information technology for civil infrastructure health monitoring. Central to this research is to handle real-world situations and uncertainties, namely noise-contaminated data and nonunique system identification results. The attempts are two folds: In terms of data processing and interpretation, novel and ambitious ideas including several types of transparent and engineered Artificial Neural Networks (ANNs) and other techniques (such as improved ERA/OKID, demystified HHT/EMD) will be tested to 1) identify structural nonlinearities, 2) de-noise and 3) overcome nonuniqueness. In terms of sensors and sensor network configurations, existing commercially available sensors will be compared in terms of performance for civil engineering applications with the focus on practical issues related to wireless MEMS sensors applications. New sensor network design methodology will be developed with concentration on solving noise contamination and nonuniqueness. With respect to broader impacts, this SGER project will facilitate cross-institutional and cross-regional communications and partnership by bringing together researchers at OU and MIT. Research results will be disseminated widely in publications and academic conferences. Development of algorithms will be carried out in a generic form so that the research will impact a wide range of applications involving data mining. The proposed research will promote applications of state-of-the-art technologies to traditional fields such as civil engineering. This research will help strengthen the safety of our nation's civil infrastructures and improve preparedness for natural and man-made hazards.

该SGER将支持用于结构健康监测（SHM）的无线传感器网络的探索性研究，重点是处理噪声污染数据测量和非唯一系统识别结果，这两个主要问题已被确定为在快速增长的传感器网络，特别是无线传感器网络的背景下最关键和最迫切的需求。提案中提出了未经测试的激进新颖想法，而分析开发以及数值和实验验证将在项目中进行。因此，该项目属于探索性、高风险项目。该项目研究将由俄克拉荷马大学（OU）和麻省理工学院（MIT）合作进行。公开大学的PI将建议麻省理工学院的博士生进行研究工作的关键部分。每年夏天，实验研究将在OU进行，PI的团队和麻省理工学院的学生将参与其中，而分析和数值工作将在两个学年中进行。主要的研究思路源于PI在哥伦比亚大学成功的透明和工程人工神经网络（ANNs）博士工作，其发展计划在广度和深度上都有大幅扩展。作为PI在传感器网络设计和数据解释方面的新想法的概念验证，结果将用于建立PI的学术证书，以便她将来提交NSF CAREER资助和参与未来的主要NSF征集，如。传感器和传感器网络…作为美国国立大学在NEES联盟的个人成员和机构候补代表，PI不仅将在该项目中开发的思想和方法应用于SHM，还将应用于地震工程，特别是与NSF NEES相关的活动。拟议活动的智力价值包括推进关于民用基础设施健康监测的传感器技术和信息技术的知识。本研究的核心是处理现实世界的情况和不确定性，即噪声污染的数据和非唯一的系统识别结果。这些尝试有两个方面：在数据处理和解释方面，将测试一些新颖而雄心勃勃的想法，包括几种类型的透明和工程人工神经网络（ann）和其他技术（如改进的ERA/OKID，去神秘化的HHT/EMD），以1)识别结构非线性，2)去噪和3)克服非唯一性。在传感器和传感器网络配置方面，将比较现有商用传感器在土木工程应用方面的性能，重点关注与无线MEMS传感器应用相关的实际问题。新的传感器网络设计方法将集中于解决噪声污染和非唯一性。就更广泛的影响而言，这个SGER项目将通过汇集开放大学和麻省理工学院的研究人员，促进跨机构和跨地区的交流和伙伴关系。研究成果将在出版物和学术会议上广泛传播。算法的开发将以通用形式进行，以便研究将影响涉及数据挖掘的广泛应用。拟议的研究将促进最先进技术在土木工程等传统领域的应用。这项研究将有助于加强我国民用基础设施的安全，提高对自然和人为灾害的准备。