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Aircraft failure detection and identification over an extended flight envelope using an artificial immune system

Aircraft failure detection and identification over an extended flight envelope using an... Abstract An integrated artificial immune system-based scheme that can operate over extended areas of the flight envelope is proposed in this paper for the detection and identification of a variety of aircraft sensor, actuator, propulsion, and structural failures/damages. A hierarchical multi-self strategy has been developed in which different self configurations are selected for detection and identification of specific abnormal conditions. Data collected using a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the scheme. The aircraft model represents a supersonic fighter, including model-following direct adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The proposed detection scheme achieves low false alarm rates and high detection and identification rates for all the categories of failures considered. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Aeronautical Journal Cambridge University Press

Aircraft failure detection and identification over an extended flight envelope using an artificial immune system

The Aeronautical Journal , Volume 115 (1163): 13 – Jan 27, 2016

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Publisher
Cambridge University Press
Copyright
Copyright © Royal Aeronautical Society 2011
ISSN
0001-9240
eISSN
2059-6464
DOI
10.1017/S0001924000005352
Publisher site
See Article on Publisher Site

Abstract

Abstract An integrated artificial immune system-based scheme that can operate over extended areas of the flight envelope is proposed in this paper for the detection and identification of a variety of aircraft sensor, actuator, propulsion, and structural failures/damages. A hierarchical multi-self strategy has been developed in which different self configurations are selected for detection and identification of specific abnormal conditions. Data collected using a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the scheme. The aircraft model represents a supersonic fighter, including model-following direct adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The proposed detection scheme achieves low false alarm rates and high detection and identification rates for all the categories of failures considered.

Journal

The Aeronautical JournalCambridge University Press

Published: Jan 27, 2016

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