Collaborative Research: CPS: Medium: Mutualistic Cyber-Physical Interaction for Self-Adaptive Multi-Damage Monitoring of Civil Infrastructure

合作研究：CPS：中：土木基础设施自适应多损伤监测的互信息物理交互

• 批准号: 2305883
• 负责人: Nora El-Gohary
• 金额: $ 50.6万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2305883&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Mutualistic

This project aims to enable mutualistic interaction of cyber damage prognostics and physical reconfigurable sensing for mutualistic and self-adaptive cyber-physical systems (CPS). Drawing inspiration from mutualism in biology where two species interact in a way that benefits both, the cyber and the physical interact in a way that they simultaneously benefit from and contribute to each other to enhance the ability of the CPS to predict, reconfigure, and adapt. Such interaction is generalizable, allowing it to enhance CPS applications in various domains. In the civil infrastructure systems domain, the mutualistic interaction-enabled CPS will allow for reconfiguring a single type of sensor, adaptively based on damage prognostics, to monitor multiple classes of infrastructure damages – thereby improving the cost-effectiveness of multi-damage infrastructure monitoring by reducing the types and number of sensors needed and maximizing the timeliness and accuracy of damage assessment and prediction at the same time. Enabling cost-effective multi-damage monitoring is promising to leapfrog the development of safer, more resilient, and sustainable infrastructure, which would stimulate economic growth and social welfare for the benefit of the nation and its people. This project will also contribute to NSF’s commitment to broadening participation in engineering (BPE) by developing innovative, interdisciplinary, and inclusive BPE programs to attract, train, and reward the next-generation engineering researchers and practitioners who are capable creators of CPS technology and not only passive consumers, thereby enhancing the U.S. economy, security, and well-being.The envisioned CPS includes three integrated components: (1) data-driven, knowledge-informed deep learning methods for generalizable damage prognostics to predict the onset and propagation of infrastructure damages, providing information about target damages to inform reconfigurable sensing, (2) signal difference maximization theory-based reconfigurable sensing methods to optimize and physically control the configurations of the sensors to actively seek to monitor each of the predicted target damages, providing damage-seeking feedback to inform damage prognostics, and (3) quality-aware edge cloud computing methods for efficient and effective damage information extraction from raw sensing signals, serving as the bridge between damage prognostics and reconfigurable sensing. The proposed CPS will be tested in multi-damage monitoring of bridges using simulation-based and actual CPS prototypes, and would be generalized to monitoring other civil infrastructure in the future. The proposed CPS methods have the potential to transform the way we design, create, and operate CPS to enable the next-generation CPS that have greater predictive ability, reconfigurability, and adaptability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在从生物学中的互利共生中汲取灵感，实现网络损害预测和物理可重构传感的互利交互，以实现互利和自适应网络物理系统（CPS）。它们以一种同时受益并相互促进的方式进行物理交互，以增强 CPS 的预测、重新配置和适应能力。这种交互是可推广的，使其能够增强 CPS 在民用基础设施系统中的应用。域中，支持互惠交互的 CPS 将允许根据损坏预测自适应地重新配置单一类型的传感器，以监测多种类别的基础设施损坏，从而通过减少类型和数量来提高多损坏基础设施监测的成本效益同时最大限度地提高损害评估和预测的及时性和准确性，实现具有成本效益的多重损害监测有望跨越发展更安全、更有弹性和可持续的基础设施，从而刺激经济增长和社会发展。该项目还将通过开发创新、跨学科和包容性的 BPE 项目来吸引、培训和奖励下一代工程研究人员，从而为 NSF 扩大工程参与 (BPE) 的承诺做出贡献。和专业人士，他们是 CPS 技术的有能力的创造者，而不仅仅是被动的消费者，从而增强美国的经济、安全和福祉。设想的 CPS 包括三个集成部分：（1）数据驱动、以知识为基础的深度学习方法可概括的损坏预测，以预测基础设施损坏的发生和传播，提供有关目标损坏的信息，以通知可重构传感，（2）基于信号差异最大化理论的可重构传感方法，以优化和物理控制传感器的配置，以主动寻求监测每个预测的目标损坏，提供损坏寻求反馈以告知损坏预测，以及（3）质量感知边缘云计算方法，用于从原始传感信号中高效且有效地提取损坏信息，作为损坏预测和损坏预测之间的桥梁。所提出的 CPS 将使用基于仿真和实际的 CPS 原型在桥梁的多损伤监测中进行测试，并将在未来推广到监测其他民用基础设施。所提出的 CPS 方法有可能改变我们的方式。设计、创建和运营 CPS，以实现具有更强预测能力、可重构性和适应性的下一代 CPS。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，认为值得支持。影响审查标准。