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

CPS：CPS：小型：一个统一的分布式时空信号处理框架的结构健康认证该奖项是根据2009年美国复苏和再投资法案资助（公法111-5）。这项研究的目标是通过开发一个统一的信号处理框架，将时空传感数据与基于物理和数据的传感器耦合起来，以满足全球对提高安全性和降低重要基础设施维护成本的迫切需求。驱动模型该方法的结构沿着以下推力：调查的可行性统计建模的动态结构，以解决时空相关的传感数据;开发有效的分布式损伤检测和定位算法;调查网络增强通过战略传感器的位置;解决最佳的传感器协作递归本地化结构状态估计和预测。与目前最先进的结构健康监测方法相比，这种创新的统一框架方法具有更可靠、更高效、更好的可扩展性的潜力。拟议的研究也是实用的，因为它允许分析真实世界的数据，这些数据考虑了传感器的结构特性、环境噪声和完整性损失。重大损害的概率表示允许进行更多信息的风险评估。更广泛的影响：该项目的成果将为安全性和可靠性迈出重要一步，尽管动态系统的复杂性不断增加。在这个项目中开发的新模型和算法是通用的，可以在许多其他领域和应用，涉及分布式递归状态估计，分布式变化检测和数据融合作出贡献。该项目将作为一个优秀的教育平台，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两级支助女生和美洲原住民学生等代表性不足的群体。

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.