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)/合作研究基金的研究目标，集中在推导科学原理，利用来自自我可持续传感器的大型网络的信息，用于工程系统的质量和完整性监测，特别是那些在制造和服务企业的核心。目标包括：传感器的制造和适配，该传感器集成了可以从宿主环境中收集能量的压电微型发电机组件，以及用于将测量信号作为射频波形传输的组件和用于测量宿主结构的振动和声发射的传感器组件；推导了一种基于非线性动态原理增强当前统计和机器学习监测基础的方法，以破译埋藏在可想象的微弱信号中的信息，这些信号来自自持续传感器网络，用于监测大型复杂系统的服役完整性；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规模复杂的民用、生物医学、机电、运输、电信和环境系统创造新的途径。它将有助于克服传感大规模复杂系统的两大技术障碍，消除布线、通电和局部处理的需要，并允许识别复杂的模式，包括非线性动力学，隐藏在现实世界传感器网络的信号中。这项研究将导致一个新的跨学科的研究生课程辅修微型无线传感网络，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.