SST: Robust Wireless Piezoelectric Sensor Network for Structural Health Monitoring

本项目是康涅狄格大学（Grant 0428210）、哥伦比亚大学（Grant 0427353）和德克萨斯工程实验站（Grant 0427878）三方协同传感器研究计划的一个组成部分。该研究旨在开发一种具有增强传感器系统鲁棒性、可靠性和检测灵敏度的无线压电传感器网络及其相关决策算法，用于结构健康监测。为了提高探测灵敏度，设计了智能自适应传感器节点，实现了基于多共振被动频率响应的探测和主动损伤询问。为了提高网络的鲁棒性，研究了分布式数据解码、分布式干扰避免和鲁棒路由策略。为了减少误报，建立了自适应统计阈值，并开发了传感器自诊断技术。来自这三个机构的机械、电气和工业工程领域的研究人员组成了一个多学科团队来开展这项工作。该项目预计将为下一代大型结构健康监测创造一种使能技术，因此对一系列工业/军事系统具有战略重要性。该技术可以大大减少系统停机时间和服务工作量。这项研究将为所有参与的学生提供多学科的培训。研究成果将应用于本科/研究生课程，并加强所有合作大学的实验室发展。这是一个由传感器倡议NSF 04-522支持的项目。

Abstract0428210PI: Jiong TangUniversity of ConnecticutThis project is one component of a 3-way collaborative sensors research program, between University of Connecticut (Grant 0428210), Columbia University (Grant 0427353) and Texas Engineering Experiment Station (Grant 0427878). The research is aimed at developing a wireless piezoelectric sensor network as well as the associated decision making algorithms with enhanced sensor system robustness, reliability, and detection sensitivity for structural health monitoring.To improve the detection sensitivity, a smart adaptive sensor node is designed to allow multi-resonance passive frequency response based detection and active damage interrogation. To enhance the network robustness, distributed data decoding, distributed interference avoidance, and robust routing strategies are investigated. To reduce false alarms, an adaptive statistical threshold is established and sensor self-diagnosis techniques are developed. A multidisciplinary team has been formed by researchers from Mechanical, Electrical, and Industrial Engineering fields from these three institutions to carry out the work. This project is expected to create an enabling technology for next-generation large-structure health monitoring and thus have strategic importance to a series of industrial/military systems. The technology could substantially reduce system downtime and service workload. This research will provide multidisciplinary training for all participating students. Research accomplishments will be implemented into undergraduate/graduate curricula and also enhance laboratory developments in all partner universities.This is a project supported under Sensors Initiative NSF 04-522.