DocumentCode :
358219
Title :
Fault detection of redundant systems based on B-spline neural network
Author :
Jin, Hong ; Chan, C.W. ; Zhang, H.Y. ; Yeung, W.K.
Author_Institution :
Dept. of Autom. Control, Beijing Univ. of Aeronaut. & Astronaut., China
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
1215
Abstract :
The fault detection and isolation of redundant sensor systems based on B-spline neural networks is presented. The network is trained using an algorithm with an adaptive learning rate. To further save computation time, the residual vector is transformed from a multivariate B-spline function to a univariate B-spline function. The detection of abrupt and drifting faults using the proposed method is discusses. The performance of the proposed method is illustrated by an example involving a redundant system consisting of six sensors
Keywords :
fault diagnosis; fuzzy neural nets; learning (artificial intelligence); redundancy; splines (mathematics); B-spline neural network; adaptive learning; fault detection; fault isolation; fuzzy neural networks; redundant system; Covariance matrix; Fault detection; Mechanical engineering; Multi-layer neural network; Neural networks; Noise measurement; Optimized production technology; Sensor systems; Spline; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 2000. Proceedings of the 2000
Conference_Location :
Chicago, IL
ISSN :
0743-1619
Print_ISBN :
0-7803-5519-9
Type :
conf
DOI :
10.1109/ACC.2000.876693
Filename :
876693
Link To Document :
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