NDLI: Fault diagnostic using improved CDKF and neural network for attitude sensor of satellite

Content Provider	IEEE Xplore Digital Library
Author	Dong Xinyuan Wang Sufeng Wu Jinjie
Copyright Year	2012
Description	Author affiliation: Inst. of Comput. Sci., Nat. Univ. of Defense Technol., Changsha, China (Dong Xinyuan; Wang Sufeng) \|\| Inst. of Aerosp. & Mater. Eng., Nat. Univ. of Defense Technol., Changsha, China (Wu Jinjie)
Abstract	The main aim of this paper is to develop a superior fault detection and isolation scheme (FDI) for the attitude sensor of a satellite. Towards this end, we present a generic data-driven prognostics framework utilized improved central divided-difference Kalman filter (CDKF) algorithm proposed in our previous work for fault detection and combine RBF neural network with fuzzy logic for fault identification process. The proposed method is applied to the micro satellite which employs three-axis magnetometer (TAM) and fiber optic gyroscope (FOG) as attitude sensors. Two types of the typical sensor fault scenarios are considered in the paper. The simulation studies demonstrate that our method shows better performance and capabilities, and thus is a good candidate for on-board fault diagnosis scheme for attitude sensors of satellite.
Sponsorship	IEEE Harbin. Sect.
Starting Page	33
Ending Page	39
File Size	485556
Page Count	7
File Format	PDF
ISBN	9781467329637
e-ISBN	9781467329644
DOI	10.1109/ICCSNT.2012.6525885
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis RBF neural network Gyroscope Central divided-difference Kalman filter Attitude sensor
Content Type	Text
Resource Type	Article

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