CPS:Small: A Unified Distributed Spatiotemporal Signal Processing Framework for Structural Health Monitoring

CPS:Small：用于结构健康监测的统一分布式时空信号处理框架

• 批准号: 0932297
• 负责人: Qi Cheng
• 金额: $ 32.66万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0932297&HistoricalAwards=false
• 关键词: CPS; Small; Unified; Distributed; Spatiotemporal

CPS: CPS:Small: A Unified Distributed Spatiotemporal Signal Processing Framework for Structural Health MonitoringThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this research is to meet the urgent global need for improved safety and reduced maintenance costs of important infrastructures by developing a unified signal processing framework coupling spatiotemporal sensing data with physics-based and data-driven models. The approach is structured along the following thrusts: investigating the feasibility of statistical modeling of dynamic structures to address the spatiotemporal correlation of sensing data; developing efficient distributed damage detection and localization algorithms; investigating network enhancement through strategic sensor placement; addressing optimal sensor collaboration for recursive localized structural state estimation and prediction.Intellectual merit: This innovative unified framework approach has the potential of being more reliable and efficient with better scalability compared to the current state-of-the-art in structural health monitoring. The proposed research is also practical as it allows analysis of real-world data that accounts for structural properties, environmental noise, and loss of integrity over sensors. Probabilistic representation of significant damages allows more informative risk assessment. Broader impacts: The outcome of this project will provide an important step toward safety and reliability albeit increasing complexity in dynamic systems. New models and algorithms developed in this project are generic and can contribute in many other areas and applications that involve distributed recursive state estimation, distributed change detection and data fusion. This project will serve as an excellent educational platform to educate and train the next generation CPS researchers and engineers. Under-represented groups such as female students and Native American students will be supported in this project, at both the graduate and undergraduate levels.

CPS：CPS：Small：用于结构健康监测的统一分布式时空信号处理框架该奖项由 2009 年美国复苏和再投资法案（公法 111-5）资助。这项研究的目的是通过开发一个将时空传感数据与基于物理和物理的数据相结合的统一信号处理框架，满足全球对提高重要基础设施的安全性和降低维护成本的迫切需求。 数据驱动模型。该方法的结构遵循以下主旨：研究动态结构统计建模以解决传感数据时空相关性的可行性；开发高效的分布式损伤检测和定位算法；研究通过战略传感器放置来增强网络；解决递归局部结构状态估计和预测的最佳传感器协作问题。智力价值：与当前最先进的结构健康监测相比，这种创新的统一框架方法具有更可靠、更高效和更好的可扩展性的潜力。拟议的研究也很实用，因为它可以分析现实世界的数据，这些数据解释了结构特性、环境噪声和传感器完整性的损失。重大损害的概率表示可以提供更多信息的风险评估。更广泛的影响：尽管动态系统的复杂性不断增加，但该项目的成果将为安全性和可靠性迈出重要一步。该项目中开发的新模型和算法是通用的，可以在涉及分布式递归状态估计、分布式变化检测和数据融合的许多其他领域和应用中做出贡献。该项目将作为一个优秀的教育平台来教育和培训下一代 CPS 研究人员和工程师。该项目将在研究生和本科生层面为女性学生和美国原住民学生等代表性不足的群体提供支持。