A New Generation of Sensor Designs based on Nonlinear Distortion and Signal Recovery for Health Assessment, Distributed Sensing and Control

亚历山大G. Parlos、Suhada Jayasuriya和Won-jong Kim，德克萨斯&农工大学提案编号：0097719提案标题：基于非线性失真和信号恢复的健康评估，分布式传感和控制项目的新一代传感器设计摘要：本研究项目解决了智能传感器设计的新方法的开发和实验演示及其在健康评估，分布式传感和反馈控制中的应用。这项工作是基于这样的理念，传感器的成本直接与其带宽，传感器的带宽应该是足够的预期目的。在传感器仅用于监测的情况下，其带宽由信号内容决定。然而，当测量的信号被并入反馈回路时，传感器必须具有比控制器的带宽大得多的带宽，控制器的带宽又必须比所需的闭环系统带宽大得多。电流传感器设计的一个固有限制是需要保持线性。拟议的研究将离开这个旧的范例，并考虑在传感器硬件中故意引入非线性特性，使自校准。使用传感器阵列的可能性，每个传感器具有比单个传感器具有大带宽小得多的带宽，将被认为是分布式传感和高性能反馈控制系统。此外，还将寻求将这项工作中设想的智能传感器用于健康监测和基于状态的维护的方法。最后，所有这些开发将被集成到一个单一的框架中，该框架将在两个实验装置上进行测试。拟议项目的技术方法将基于智能传感器开发的非线性估计和多速率信号处理技术。控制方法将基于定量反馈理论的思想，而建议的自校准将依赖于随机建模。预计这项研究将显着推进智能传感器的最新技术水平。

AbstractPIs: Alexander G. Parlos, Suhada Jayasuriya and Won-jong Kim, Texas A&M UniversityProposal Number: 0097719Proposal Title: A New Generation of Sensor Designs based on Nonlinear Distortion and Signal Recovery for Health Assessment, Distributed Sensing and ControlProject Abstract:This research project addresses the development and experimental demonstration of a new methodology for smart sensor designs and their use in health assessment, distributed sensing and feedback control. This work is predicated on the philosophy that sensor cost is directly tied to its bandwidth and that sensor bandwidth should be just enough for an intended purpose. In the case where a sensor is only used for monitoring, its bandwidth is dictated by the signal contents. However, when measured signals are to be incorporated in a feedback loop, the sensors must have significantly larger bandwidths than the bandwidth of the controller which, in turn must be much larger than the required closed loop system bandwidth. An inherent limitation of current sensor designs is the implied need to maintain linearity. The proposed research will depart from this old paradigm and consider the deliberate introduction of nonlinear characteristics in the sensor hardware, enabling self-calibration. The possibility of using arrays of sensors, each with a much smaller bandwidth than a single sensor with large bandwidth, will be considered both for distributed sensing and for high performance feedback control systems. Furthermore methods for integrating the smart sensors envisioned in this work for health monitoring and condition-based maintenance will be pursued. Finally, all of these developments will be integrated into a single framework that will be tested on two experimental setups. The technical approach of the proposed project will be based on nonlinear estimation and multirate signal processing techniques for smart sensor development. The control methodology will be based on ideas from Quantitative Feedback Theory, whereas the proposed self-calibration will rely on stochastic modeling. It is expected that this research will significantly advance the state of the art in smart sensors.