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

这个传感器小团队（SST）/合作研究资助的研究目标集中在科学原理的推导，用于利用来自大型自我可持续传感器网络的信息，用于工程系统的质量和完整性监测，特别是那些处于制造和服务企业核心的传感器。这些目标包括：传感器的制造和适配，所述传感器将能够从主体环境收集能量的压电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被调谐以测量来自主体结构的振动和声发射的传感器部件集成;基于用非线性动态原理增强统计学和机器学习的当前监测基础的方法的推导破译隐藏在可以想象的微弱信号中的信息，这些信号来自一个自我维持的传感器网络，用于监测大型复杂系统的服务完整性;并通过实验室实验和有限的现场测试验证了管道基础设施在役完整性监测的新框架。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。它将有助于克服两个主要的技术障碍，通过消除布线，通电和本地处理的需要，并允许识别复杂的模式，包括非线性动力学，潜在的信号从现实世界的传感器网络。这项研究将导致一个新的跨学科的研究生课程未成年人在微型无线传感网络，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.