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

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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.