Algorithm-Fused High Performance Damage Detector: Optimal Sensor Distributions

算法融合的高性能损伤检测器：最佳传感器分布

• 批准号: 1000391
• 负责人: Dionisio Bernal
• 金额: $ 13万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1000391&HistoricalAwards=false
• 关键词: Algorithm; Fused; Performance; Damage; Detector

The project will focus the development of a robust automated approach for identifying damage in structural systems. The research thrusts are: 1) fusion of complementary algorithms and 2) optimal sensor distributions for the fused set. Selection of complementary algorithms involves identification of methods whose sensitivity to damage and to the sources that cloud damage detection differs with damage scenarios and operating conditions. In a first phase the project inspects the fusion of detection filters that operate on residual correlations with filters that work with amplitude dependent residual metrics. The research expects to demonstrate that the optimized fused detector will have a damage detection threshold that, for a fixed probability of false alarm, is significantly better than that of the individual algorithms. Intimately connected with the algorithmic fusion is research on the selection of sensor layouts that are optimal, given the fused interrogation scheme. Following the analytical work the research progresses into an experimental phase where the performance of the fused algorithms is tested on a one quarter scale steel structure that is exposed to the weather and thus subjected to realistic environmental changes.Algorithm fusion has proven fruitful in Automatic Target Recognition and various other areas but a systematic examination in the context of Structural Health Monitoring is first carried out in this project. If successful, this research will not only offer a robust damage detection scheme for applications to civil structures but it will also point to the merit of algorithmic fusion for other objectives such as the localization and the quantification of damage. Educational activities connected with the project include: 1) interactions with Olin College, an undergraduate engineering school of excellence, through introduction of multi-week research activities based on topics from the project 2) involvement with the program Girls Get Connected (GGC), a science and technology outreach for middle school girls in the Boston area and 3) an afternoon of hands-on activities on the Harvard?s Medical School explorations program, which is attended each fall by over 200 middle school students from Cambridge and Boston. The graduate student working on the project will also receive advanced training on the topic of damage detection in civil structures which is of high engineering importance.

该项目将重点开发一种强大的自动化方法来识别结构系统中的损伤。研究的重点是：1)互补算法的融合；2)融合集的最优传感器分布。补充算法的选择涉及识别对损伤和云损伤检测来源的敏感性随损伤场景和操作条件的不同而不同的方法。在第一阶段，该项目检查检测滤波器的融合，这些滤波器对残差相关性进行操作，对依赖于振幅的残差度量进行处理。研究期望证明，在虚警概率固定的情况下，优化后的融合检测器具有明显优于单个算法的损伤检测阈值。与算法融合密切相关的是研究在给定融合询问方案的情况下，如何选择最优的传感器布局。在分析工作之后，研究进展到实验阶段，在四分之一的钢结构上测试融合算法的性能，该结构暴露在天气中，因此受到现实环境变化的影响。算法融合在自动目标识别和其他领域已经被证明是卓有成效的，但在结构健康监测的背景下，本项目首次进行了系统的研究。如果成功的话，这项研究不仅将为土木结构的应用提供一个强大的损伤检测方案，而且还将为其他目标（如损伤的定位和量化）指出算法融合的优点。与该项目相关的教育活动包括：1)与奥林学院（一所优秀的本科工程学院，通过介绍基于项目主题的为期数周的研究活动进行互动；2）参与女孩互联计划（GGC），这是一项针对波士顿地区中学女孩的科技推广活动；3)一个下午在哈佛大学？每年秋天都有来自剑桥和波士顿的200多名中学生参加。从事该项目的研究生还将接受土木结构损伤检测方面的高级培训，这在工程上具有很高的重要性。