DocumentCode :
1614714
Title :
Fault diagnosis based on second-order Taylor series dynamic prediction for autonomous underwater vehicle sensor
Author :
Qilong Jia ; Jinxue Xu ; Guofeng Wang
Author_Institution :
Sch. of Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China
fYear :
2013
Firstpage :
651
Lastpage :
655
Abstract :
A sensor fault diagnosis method based on second-order Taylor series dynamic prediction for autonomous underwater vehicle is proposed. Its principle and implementing steps for sensor fault diagnosis are described in detail. The method can improve the prediction accuracy comparing with grey dynamic prediction approach. Thus, the possibility of misjudgment can be decreased. The simulation results demonstrate the effectiveness of it for four typical fault models of autonomous underwater vehicle sensors. In addition, the prediction data can replace the failure data to recover the signal.
Keywords :
autonomous underwater vehicles; fault diagnosis; grey systems; sensors; series (mathematics); signal reconstruction; AUV fault models; autonomous underwater vehicle sensor; grey dynamic prediction approach; second-order Taylor series dynamic prediction; sensor fault diagnosis; signal recovery; Data models; Educational institutions; Fault diagnosis; Mathematical model; Predictive models; Taylor series; autonomous underwater vehicle; data driven; dynamic prediction; fault diagnosis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Chinese Automation Congress (CAC), 2013
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-0332-0
Type :
conf
DOI :
10.1109/CAC.2013.6775815
Filename :
6775815
Link To Document :
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