SST: Robust Wireless Piezoelectric Sensor Network for Structural Health Monitoring

本项目是康涅狄格大学(0428210)、哥伦比亚大学(0427353)和德克萨斯工程实验站(0427878)三方协作传感器研究项目的一个组成部分。为提高结构健康监测的检测灵敏度，设计了基于多共振被动频率响应的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检测和主动损伤查询。为了增强网络的健壮性，研究了分布式数据译码、分布式干扰避免和健壮路由策略。为了减少虚警，建立了一个自适应的统计阈值，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项使能技术，从而对一系列工业/军事系统具有战略重要性。该技术可以大幅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。研究成果将被落实到本科/研究生课程中，并加强所有伙伴大学的实验室发展。这是一个由传感器倡议NSF 04-522支持的项目。

Abstract0427878PI: Yu DingTexas Engineering Experiment StationThis 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.