CSR: Medium: Self-organizing Cyber Substrates: Exploring a Modular Computing and Communications Architecture for Structural Health Monitoring

许多物理结构，包括飞机机翼、桥梁、风力涡轮机叶片、水坝和建筑物，随着时间的推移会发生裂缝。及早发现这些问题对于防止重大灾难至关重要。一个有前途的方法是安装传感器，连续监测和报告结构的完整性。然而，由于当前技术的几个限制，包括需要频繁更换电池供电的传感器的电池，无源传感器的有限的询问范围，以及通常涉及射频传感器网络的单独的通信子系统，采用受到限制。本项目旨在通过探索一系列新技术来消除这些障碍，包括利用结构本身的基底进行机械能量收集和超声通信。本研究的长期愿景是开发一种变革性的自组织嵌入式系统设计范式，促进自供电计算，自供电网络和结构健康监测到“网络基底”的融合，可以“感知”、“感觉”和“诊断”即将发生的灾难性故障。该方法基于一种新的集成传感，信号处理和通信芯片组，它可以从它所连接或嵌入的基板上清除应变和振动能量。能量在电容器中积累，用于计算和通信。这些芯片能够进行足够的本地信号处理以检测指示故障的事件，使得它们的通信要求被降低到每个事件向日志记录设备发送单个比特。该项目的主要创新在于以下领域：1）用于超低功率预算的感测、事件分类和信号处理的嵌入式计算硬件，2）用于极端能量经济的无分组和贯穿衬底脉冲联网，3）从衬底结构收集能量，4）用于结构健康评估和事件预测的二进制计算模型，5）智能嵌入式软件，用于所有上述元素的自组织集成。所有这些组件都是围绕二进制信息框架的核心主题设计的，该框架在传感，通信和结构处理的全局推理算法中被大量利用以进行功率优化。 教育和推广活动包括不同的研究生，本科生和高中学生参与研究，并开发一个新的跨学科的结构健康监测研究生课程。

Many physical structures, including airplane wings, bridges, wind turbine blades, dams, and buildings, are subject to the development of fractures over time. Detecting these problems early is essential to preventing major catastrophes. A promising approach is to install sensors that continuously monitor and report on the structure's integrity. However, adoption has been limited because of several limitations in current technology, including a need for frequent battery replacement for battery-powered sensors, limited interrogation range of passive sensors, and a separate communication subsystem usually involving radio frequency sensor networks. This project aims to remove these barriers by exploring a set of new technologies, including mechanical energy harvesting and ultra-sound communication using the substrate of the structure itself.The long term vision of this research is to develop a transformative self-organizing embedded system design paradigm that facilitates convergence of self-powered computing, self-powered networking and structural health monitoring into "cyber-substrates", that can "sense", "feel" and "diagnose" impending catastrophic failures. The approach is based on a new integrated sensing, signal processing, and communication chipset, which scavenges both strain and vibration energy from the substrate to which it is attached or embedded into. Energy is accumulated in a capacitor for computing and communications. These chips are capable of enough local signal processing to detect events indicative of failures, so that their communication requirement is reduced to sending a single bit per event to a logging device. Key innovations in this project are in the areas of: 1) embedded computing hardware for sensing, event classification, and signal processing with ultra-low power budgets, 2) packet-less and through-substrate pulse networking for extreme energy economy, 3) energy scavenging from the substrate structure, 4) binary computation models for structural health evaluation and event predictions, and 5) smart embedded software for self-organizing integration of all the above elements. All these components are designed around the core theme of binary information framework which is heavily leveraged for power optimization in sensing, communication, and global inference algorithms for structural processing. Education and outreach activities include involvement of diverse graduate, undergraduate and high school students in the research, and development of a new interdisciplinary graduate course in structural health monitoring.