CPS: Breakthrough: Mobile Automated Rovers Fly-By (MARS-FLY) for Bridge Network Resiliency

本文的目的是研究移动自动探测车（MARS-FLY）的桥梁网络弹性。桥梁通常位于偏远地区，在数十万座桥梁上安装电力和数据采集系统的成本令人望而却步。MARS-FLY项目将开发一种网络物理系统（CPS），用于监测公路桥梁的健康状况，控制重型卡车对桥梁施加的载荷，并为可视化检查员提供数据驱动的桥梁健康评估的定量信息，无需电力和现场最少的数据采集电子设备。对于飞行监控，gps控制的自动驾驶无人机将定期携带数据采集电子设备到桥上，并在近距离从传感器下载数据。携带红外（IR）摄像机的大型成像无人机将用于检测混凝土分层等细节损伤。研究目标将首先通过无人机对遥感器进行无线充电来提高传感器的使用寿命，而无人机电池的无线充电将延长无人机的运行效率、有效载荷和航程。这种新颖的多线圈无线供电方法将提供一种工程材料，即具有谐振链路的超材料，以提高功率水平和链路距离，否则无法实现。其次，在利用少量低质量传感器数据和红外图像来确定各级损伤信息方面取得重大科学突破：检测行为、位置和大小的变化；基于集成经验模态分解和希尔伯特变换等数值与概率相结合的方法，简化可靠性分析，将新的损伤信息纳入桥梁的可靠度指标；最后在贝叶斯更新框架中检测信号中的非线性。此外，一辆仪器驱动的车辆将补充桥梁上的损伤检测。一个贝叶斯更新框架将用于更新给定测量值的桥梁状态的概率分布。对红外图像进行图像处理，以区分环境影响和真正的桥梁退化（例如混凝土分层），从而开发出更好的特定地点和特定环境的校准方法。

This proposal is for research on the Mobile Automated Rovers Fly-By (MARS-FLY) for Bridge Network Resiliency. Bridges are often in remote locations and the cost of installing electricity and a data acquisition system in hundreds of thousands of bridges is prohibitive. The MARS-FLY project will develop a cyber-physical system (CPS) designed to monitor the health of highway bridges, control the loads imposed on bridges by heavy trucks, and provide visual inspectors with quantitative information for data-driven bridge health assessment requiring no electricity and a minimum of data acquisition electronics on site. For fly-by monitoring, GPS-controlled auto-piloted drones will periodically carry data acquisition electronics to the bridge and download the data from the sensors at a close range. Larger Imaging drones carrying infrared (IR) cameras will be used to detect detail damages like concrete delamination. The research objectives will be accomplished first, by wireless recharging of remote sensor motes by drone to enhance the sensor operational lifetime whereas wireless recharging of drone battery will extend the operational efficiency, payload, and drone range. The novel multi-coil wireless powering approach will provide an investigation of an engineered material i.e. metamaterial with the resonant link to enhance the power level and link distance, otherwise unachievable. Next, by a major scientific breakthrough in the utilization of small quantities of low quality sensor data and IR images to determine damage information at all levels: detection of a change in behavior, location, and magnitude; streamlining of reliability analysis to incorporate the new information of damage into the bridge's reliability index based on combined numerical and probabilistic approaches such as Ensemble Empirical Mode Decomposition with the Hilbert Transform; and finally detection of nonlinearities in the signals in a Bayesian Updating framework. Moreover, an instrumented drive-by vehicle will complement damage detection on the bridge. A Bayesian updating framework will be used to update the probability distribution for bridge condition, given the measurements. Image processing of the infrared images to distinguish between the environmental effects and the true bridge deterioration (e.g. delamination in concrete) will be used to develop a better method of site-specific and environment-specific calibration.