SST/Collaborative Research: Self-Supporting Wireless Sensor Networks for In-Process and In-Service Integrity Monitoring Using High Energy-Harvesting Nonlinear Modeling Principles

这一传感器小型团队(SST)/合作研究基金的研究目标侧重于为利用来自自给自足的大型传感器网络的信息来进行工程系统的质量和完整性监测而制定科学原则，特别是那些处于制造和服务企业核心的系统。目标包括：制造和改装传感器，将可从宿主环境中获取能量的压电式微型发电机部件与用于将测量信号作为射频波形传输的部件和用于测量主机结构的振动和声发射的传感器部件相结合；推导出一种方法，其基础是用非线性动力学原理增强统计学和机器学习的现有监测基础，以破译隐藏在可想象的微弱的自我维持传感器网络中的信息，用于监测大型复杂系统的运行完整性；并通过实验室实验和有限的现场测试验证了新的管道基础设施在役完整性监测框架。如果成功，这项研究将为监测和控制大型复杂的民用、生物医学、机电、交通、电信和环境系统开辟一条新的途径。它将有助于跨越大规模复杂系统传感的两大技术障碍，省去了布线、供电和本地处理的需要，并允许识别现实世界传感器网络信号背后的复杂模式，包括非线性动力学。这项研究将导致一个新的跨学科研究生课程，辅修微型无线传感网络，将由四个参与机构联合提供。

The research objectives of this Sensor Small Team (SST)/Collaborative Research grant focus on the derivation of scientific principles for harnessing information from a large network of self-sustainable sensors for quality and integrity monitoring in engineering systems, especially those at the core of manufacturing and service enterprises. The objectives include: fabrication and adaptation of sensors that integrate the piezoelectric micro-power generator components that can harvest energy from the host-environment with components for transmitting the measured signals as radio frequency waveforms and sensor components tuned to measure vibrations and acoustic emission from a host structure; derivation of an approach based on augmenting the current monitoring foundations of statistics and machine learning with nonlinear dynamic principles to decipher information buried in the, conceivably feeble, signals from a network of self-sustainable sensors for monitoring in-service integrity of large complex systems; and validation of the new framework for in-service integrity monitoring of pipeline infrastructure through lab experiments and limited field tests.If successful, this research will create a new pathway to monitor and control large-scale complex civil, biomedical, electro-mechanical, transportation, telecommunications, and environmental systems. It will help in surmounting two major technological barriers in sensing large scale complex systems by obviating the need for wiring, energizing and local processing, and allowing for discerning the complicated patterns, including the nonlinear dynamics, underlying the signals from real-world sensor networks. The research will lead to a novel interdisciplinary graduate program minor in Miniature Wireless Sensing Networks, to be offered jointly by the four participating institutions.