Aircraft sensor validation monitor and state estimator using artificial intelligence

Seung-Keon Kwak; Hwan-Sik Yoon

doi:10.1117/12.658768

30 March 2006 Aircraft sensor validation monitor and state estimator using artificial intelligence

Seung-Keon Kwak, Hwan-Sik Yoon

Proceedings Volume 6167, Smart Structures and Materials 2006: Smart Sensor Monitoring Systems and Applications; 61671R (2006) https://doi.org/10.1117/12.658768
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2006, San Diego, California, United States

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.

Citation Download Citation

Seung-Keon Kwak and Hwan-Sik Yoon "Aircraft sensor validation monitor and state estimator using artificial intelligence", Proc. SPIE 6167, Smart Structures and Materials 2006: Smart Sensor Monitoring Systems and Applications, 61671R (30 March 2006); https://doi.org/10.1117/12.658768

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