System Identification of Dynamically Sensitive Structures using Wireless Smart Sensor Networks

使用无线智能传感器网络对动态敏感结构进行系统识别

• 批准号: 341306-2012
• 负责人: Narasimhan, Sriram
• 金额: $ 1.53万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572226
• 关键词: System; Identification; Dynamically; Sensitive; Structures

Large stocks of buildings and bridges in North America and elsewhere are nearing their design life, or have already surpassed it. Many are vulnerable to dynamic effects (e.g., earthquakes), as their risk was initially under-estimated. We need to assess their current state in order to make sound engineering decisions on whether to replace, rehabilitate or retrofit them, and to predict their remaining life. This is possible through system identification, which combines sensor measurements obtained from the structure and mathematical procedures to yield information regarding its current state. An expensive exercise just a few years ago, wireless sensors have now made system identification on large-scale structures more affordable. There is no need for expensive wiring, and local processing capabilities on these sensors allow for hierarchical implementation of algorithms. Nevertheless, the advances in wireless sensor hardware have far out-paced our ability to effectively use and interrogate measured data. This is because, existing algorithms are sensitive to noise, not autonomous and demand large transmission loads. The main aim of this proposal is to overcome these drawbacks by developing a new class of algorithms. These algorithms will be decentralized, computationally light and autonomous, and will employ low transmission rates. This will be achieved using two powerful concepts in signal processing: time-frequency analysis and compressive sensing. The former will enable autonomous and decentralized implementations, while the latter will help us reduce data transmission. Theoretical, laboratory and field studies are proposed. This approach will lead to effective and affordable means of condition assessment to address the problem of aging infrastructure. Students will be trained in inter-disciplinary skills, which are in demand in Canada.

在北美和其他地方，大量的建筑物和桥梁正接近或已经超过其设计寿命。许多建筑物和桥梁容易受到动态效应的影响（例如，地震），因为它们的风险最初被低估。我们需要评估它们的现状，以便就是否更换、修复或改造它们做出合理的工程决策，并预测它们的剩余寿命。这可以通过系统识别来实现，系统识别将从结构和数学程序获得的传感器测量结果结合起来，以产生关于其当前状态的信息。 几年前，无线传感器还是一项昂贵的工作，现在它已经使大型结构的系统识别变得更加经济实惠。不需要昂贵的布线，这些传感器的本地处理能力允许算法的分层实现。然而，无线传感器硬件的进步已经远远超过了我们有效使用和查询测量数据的能力。这是因为，现有的算法对噪声敏感，不是自主的，并且需要大的传输负载。 该建议的主要目的是通过开发一类新的算法来克服这些缺点。这些算法将是分散的，计算量小和自治的，并且将采用低传输速率。这将使用信号处理中的两个强大概念来实现：时频分析和压缩感知。前者将实现自主和分散的实现，而后者将帮助我们减少数据传输。 提出了理论、实验室和现场研究。 这种方法将导致有效和负担得起的条件评估手段，以解决基础设施老化的问题。 学生将接受跨学科技能的培训，这是在加拿大的需求。