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

摘要/ Abstract摘要：Alexander G. Parlos， Suhada Jayasuriya和Won-jong Kim， Texas a&m university提案号：0097719提案题目：用于健康评估、分布式传感和控制的基于非线性失真和信号恢复的新一代传感器设计项目摘要：本研究项目致力于智能传感器设计的新方法的开发和实验演示，以及它们在健康评估、分布式传感和反馈控制中的应用。这项工作基于传感器成本直接与其带宽相关的理念，并且传感器带宽应该足以满足预期目的。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，当测量信号被纳入反馈回路时，传感器的带宽必须比控制器的带宽大得多，而控制器的带宽又必须比所需的闭环系统带宽大得多。当前传感器设计的一个固有限制是需要保持线性。所提出的研究将脱离这种旧的范式，并考虑在传感器硬件中故意引入非线性特性，从而实现自校准。对于分布式传感和高性能反馈控制系统，将考虑使用传感器阵列的可能性，每个传感器的带宽比单个传感器的带宽小得多。此外，将寻求将本工作中设想的智能传感器集成到健康监测和基于状态的维护中的方法。最后，所有这些发展将被整合到一个单一的框架中，并在两个实验装置上进行测试。提出的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，而提出的自校准将依赖于随机建模。预计这项研究将大大推动智能传感器的发展。

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.