Real-time fault diagnosis of satellite attitude control system based on sliding-window wavelet and DRNN

This paper proposes an online fault detecting and isolating (FDI) scheme of satellite attitude control system (SACS) based on Wavelet and Dynamic Recurrent Neural Network (DRNN) which is capable of processing time-varying signals in real time. First, a novel improved wavelet method is proposed to de...

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Bibliographic Details
Published in2010 Chinese Control and Decision Conference pp. 1218 - 1222
Main Authors Cen, Zhao-hui, Wei, Jiao-long, Jiang, Rui, Liu, Xiong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2010
Subjects
Actuators
Attitude control
DRNN
Fault detection
Fault diagnosis
Infrared sensors
Position measurement
Real time systems
Real-time fault diagnosis
Recurrent neural networks
SACS
Satellites
Signal processing
Wavelet
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Summary:This paper proposes an online fault detecting and isolating (FDI) scheme of satellite attitude control system (SACS) based on Wavelet and Dynamic Recurrent Neural Network (DRNN) which is capable of processing time-varying signals in real time. First, a novel improved wavelet method is proposed to detect faults; then, a DRNN is designed for fault isolating (FI) and the relevant fault decision module as well. The DRNN is trained by corresponding target FDI result of fault data set sampled from actuator and sensor outputs. Unlike many previous wavelet-based fault detecting methods developed in the literature, our proposed FDI scheme is only based on measurement signals which can avoid modeling, also wavelet method is improved and suitable for online signal processing. Real-time simulation is performed and the results demonstrate the validity and superiority of our method.
ISBN:1424451817
9781424451814
ISSN:1948-9439
1948-9447
DOI:10.1109/CCDC.2010.5498162