DocumentCode :
1986325
Title :
An approach to nonlinear fault diagnosis based on neural network adaptive observer
Author :
Chuan, Zhou ; Weili, Hu ; Qingwei, Chen ; Xiaobei, Wu ; Shousong, Hu
Author_Institution :
Dept. of Autom., Nanjing Univ. of Sci. & Technol., China
Volume :
4
fYear :
2002
fDate :
2002
Firstpage :
2733
Abstract :
A fault detection method based on neural network online approximation structure for uncertain nonlinear systems is presented. A neural network observer is used to learn the nonlinear fault functions to monitor the abnormal behavior of the dynamic system. When system faults occur, the online learning structure can approximate all possible unknown faults, then the faults are identified and accommodated. The uniformly ultimately bounded stability of the closed-loop error system is guaranteed by Lyapunov stability theory and the weights are tuned without need of persistency of excitation.
Keywords :
Lyapunov methods; closed loop systems; fault diagnosis; learning (artificial intelligence); neural nets; nonlinear control systems; observers; uncertain systems; Lyapunov stability theory; abnormal behavior; closed-loop error system; dynamic system; fault detection method; neural network adaptive observer; nonlinear fault diagnosis; nonlinear fault functions; online approximation structure; online learning structure; uncertain nonlinear system; uniformly ultimately bounded stability; Adaptive control; Adaptive systems; Automation; Electronic mail; Fault detection; Fault diagnosis; Neural networks; Nonlinear systems; Programmable control; Space technology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN :
0-7803-7268-9
Type :
conf
DOI :
10.1109/WCICA.2002.1020015
Filename :
1020015
Link To Document :
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