A Dual-Mode Millimeter-Wave Sensor Network for Structural Monitoring in Wind Farms

用于风电场结构监测的双模毫米波传感器网络

• 批准号: 2112003
• 负责人: Changzhi Li
• 金额: $ 55万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112003&HistoricalAwards=false
• 关键词: Dual; Mode; Millimeter; Wave; Sensor

This project will enhance the resiliency and sustainability of the Nation’s wind energy infrastructures by investigating effective and autonomous turbine inspection. The major forms of structural monitoring of wind structures have traditionally been accomplished using contact-based sensors such as accelerometers and strain gauges. However, application of these contact-based sensors is limited by challenges in onsite integration, maintenance, and reconfiguration. Light Detection and Ranging (LIDAR) systems are non-contact and have high precision, but they are susceptible to weather conditions and are often installed at fixed locations. Cameras can be mounted on unmanned aerial vehicles (UAVs) to scan the surface of turbines. However, this approach requires stopping the rotation of turbine blades and can only inspect the surface. To address these challenges, this project will develop a dual-mode millimeter-wave sensor network driven by a unified decision-making framework that optimizes structural inspection in wind farms. Since radio frequency signals are robust against ambient light and weather conditions, a stationary platform located near turbines will offer robust continuous monitoring during normal operation. On the other hand, a formation of swarm-UAV-based sensor network can synthesize a large observation aperture for high-resolution imaging when an initial problem is identified by continuous-monitoring sensors, or during scheduled maintenance. This project has an interdisciplinary nature involving millimeter-wave sensing, adaptive UAV formation, swarm flight control, and unified decision-making framework for system-level inspection schedule optimization. It will generate new knowledge and methodologies for structural health monitoring of critical infrastructures such as power transmission networks, oil/gas pipelines, and transportation networks. The project provides a valuable opportunity for students to develop their interest in the fields of system reliability optimization, autonomous robotics, and microwave/millimeter-wave technologies. The PIs will develop integrated research and education programs to attract students from underrepresented groups and K-12 students into engineering and involve undergraduate students into research. It will also encourage student entrepreneurship based on successful technology development.The project is innovative in that it integrates unique miniature millimeter-wave remote sensing capability with advanced UAV control methods for networked coherent detection. Furthermore, it unifies the long-term inspection planning at the system level and the dynamic prognosis based on the short-term information at the turbine level. This project will investigate: 1) stationary sensors mounted near wind turbines to provide uninterrupted monitoring while turbines are in operation, where analytic and machine learning methods will be integrated to analyze blade distortion from the micro-Doppler signatures generated by rotating turbine blades; 2) UAV-based sensor arrays to scan details of turbine blades with high-resolution synthetic aperture imaging; 3) novel intrinsic flight control strategies to enable swarms of UAVs to realize the desired formation with sufficient precision, energy efficiency, and minimal jitter for synthetic aperture imaging; 4) a unified decision-making framework to optimize the inspection schedule of wind turbines from a systems and dynamic perspective.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.

该项目将通过调查有效和自主的涡轮机检查来提高国家风能基础设施的弹性和可持续性。传统上，风力结构的结构监测的主要形式是使用基于接触的传感器，如加速度计和应变计来完成的。然而，这些接触式传感器的应用受到现场集成、维护和重新配置方面的挑战的限制。光探测和测距（LIDAR）系统是非接触式的，精度很高，但它们容易受到天气条件的影响，并且通常安装在固定位置。摄像机可以安装在无人机（UAV）上，以扫描涡轮机的表面。然而，这种方法需要停止涡轮机叶片的旋转，并且只能检查表面。为了应对这些挑战，该项目将开发一个由统一决策框架驱动的双模毫米波传感器网络，以优化风电场的结构检查。由于射频信号对环境光线和天气条件具有鲁棒性，因此位于涡轮机附近的固定平台将在正常运行期间提供鲁棒的连续监测。另一方面，编队无人机传感器网络可以合成一个大的观察孔径高分辨率成像时，一个初始的问题是由连续监测传感器，或在定期维护。该项目具有跨学科性质，涉及毫米波传感，自适应无人机编队，群飞行控制和系统级检查计划优化的统一决策框架。它将产生新的知识和方法，用于关键基础设施的结构健康监测，如输电网络，石油/天然气管道和运输网络。该项目为学生提供了一个宝贵的机会，培养他们对系统可靠性优化，自主机器人和微波/毫米波技术领域的兴趣。PI将开发综合研究和教育计划，以吸引来自代表性不足的群体和K-12学生的学生进入工程领域，并让本科生参与研究。该项目的创新之处在于，它将独特的微型毫米波遥感能力与先进的无人机控制方法相结合，用于网络化相干探测。该方法将系统级的长期检修计划和基于涡轮机级短期信息的动态预测统一起来。该项目将调查：1）安装在风力涡轮机附近的固定传感器，以在涡轮机运行时提供不间断的监测，其中分析和机器学习方法将被集成，以根据旋转涡轮机叶片产生的微多普勒特征来分析叶片失真; 2）基于无人机的传感器阵列，以利用高分辨率合成孔径成像来扫描涡轮机叶片的细节; 3）新颖的内在飞行控制策略，以使无人机群能够实现所需的编队，具有足够的精度、能量效率和最小的抖动，用于合成孔径成像;（4）统一决策-从系统和动态的角度制定优化风力涡轮机检查计划的框架。该奖项反映了NSF的法定使命，并被认为值得支持通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

项目成果

Advancement of PMCW Radar and Its Board-Level Prototyping

PMCW雷达及其板级原型设计的进展

• DOI: 10.1109/dcas57389.2023.10130242
• 发表时间: 2023
• 期刊: 2023 IEEE 16th Dallas Circuits and Systems Conference (DCAS
• 作者: Brown, Michael;Li, Changzhi
• 通讯作者: Li, Changzhi

A K -Band Ultra-Wideband Binary Phase Shifter for Phase Modulating Applications in Radar

用于雷达相位调制应用的 K 波段超宽带二进制移相器

• DOI: 10.1109/lmwt.2022.3230039
• 发表时间: 2023
• 期刊: IEEE Microwave and Wireless Technology Letters
• 作者: Brown, Michael C.;Li, Changzhi
• 通讯作者: Li, Changzhi