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

Pi：俄克拉荷马大学的裴进(音译)：这个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，重点是处理受噪声污染的数据测量和非唯一系统识别结果，这两个主要问题已经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积极的新想法，同时将在该项目中进行分析开发以及数值和实验验证。因此，该项目被认为是探索性和高风险的。该项目研究将通过俄克拉荷马大学(OU)和麻省理工学院(MIT)的合作进行。OU的PI将建议麻省理工学院的一名博士生进行关键的研究工作。实验研究将在OU进行，由PI的团队和麻省理工学院的学生参加，并在整个两个学年进行分析和数值工作。主要的研究思路来自于PI在哥伦比亚大学关于透明和工程化人工神经网络(ANN)的成功博士工作，其发展计划在广度和深度上都有了极大的扩展。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，结果将被用来建立PI的学术资历，以便她未来提交NSF职业资助，并参与未来NSF的主要征集，如传感器和传感器网络。作为NEES联合会的个人成员和OU的机构候补代表，PI将把本项目发展的思想和方法不仅应用于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增进关于传感器技术和信息技术的知识，以监测民用基础设施的健康状况。这项研究的核心是处理真实世界的情况和不确定性，即受噪声污染的数据和不唯一的系统识别结果。在数据处理和解释方面，将测试新的和雄心勃勃的想法，包括几种类型的透明和工程化的人工神经网络(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，重点是与无线MEMS传感器应用相关的实际问题。将开发新的传感器网络设计方法，重点解决噪声污染和不唯一性问题。关于更广泛的影响，这一SGER项目将通过汇聚俄勒冈大学和麻省理工学院的研究人员，促进跨机构和跨区域的交流和伙伴关系。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，并改善对自然和人为灾害的准备。

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.