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Proceedings Paper

Aircraft sensor validation monitor and state estimator using artificial intelligence
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Paper Abstract

A new Sensor Validity Monitoring, Verification, and Accommodation (SVMVA) technique based on an artificial neural network is developed for a self-repairing Flight Control System (FCS). For the proposed system, the Learning Vector Quantization (LVQ) method is employed as the on-line, real time learning, monitoring, and estimation tool. In order to conduct a feasibility study, we applied the developed algorithm to a flight vehicle simulator. The simulation results show that the proposed SVMVA with LVQ can instantly detect the failure of physical sensors and accommodate them for more than 30 minutes. By employing this type of analytical sensor redundancy, a flight vehicle can save power, weight, and space, which are required for installing redundant physical sensors.

Paper Details

Date Published: 30 March 2006
PDF: 9 pages
Proc. SPIE 6167, Smart Structures and Materials 2006: Smart Sensor Monitoring Systems and Applications, 61671R (30 March 2006); doi: 10.1117/12.658768
Show Author Affiliations
Seung-Keon Kwak, SenAnTech, Inc. (United States)
Hwan-Sik Yoon, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 6167:
Smart Structures and Materials 2006: Smart Sensor Monitoring Systems and Applications
Daniele Inaudi; Wolfgang Ecke; Brian Culshaw; Kara J. Peters; Eric Udd, Editor(s)

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