NDLI: The Fault Diagnosis Problem: Residual Generators Design Using Neural Networks in a Two-Tanks Interconnected System

Content Provider	IEEE Xplore Digital Library
Author	Anzurez-Marin, J. Cuevas-Silva, O. Pitalua-Diaz, N.
Copyright Year	2009
Abstract	In this work, a fault detection method based on a neural-network models bank to residual generation and a residual evaluation scheme using a fuzzy rules type is developed. The case of study is a nonlinear hydraulic system consisting of two interconnected tanks which is simulated, in normal conditions and fault conditions. In this case we use also its equivalent Takagi-Sugeno Model in discreet time. This way, the simulation provides the data to train each one of the neuronal models. The update the weights is based on the algorithm BP (Back Propagation) with a stage of scale applied to the training data in order to avoid over-training on the neural network, due to the asymptotic limits of the sigmoid function used. The results show a correct identification on the different fault scenes and it motivates us to the real implementation of faults diagnosis procedure.
Starting Page	307
Ending Page	312
File Size	480536
Page Count	6
File Format	PDF
ISBN	9780769537993
DOI	10.1109/CERMA.2009.44
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-22
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision making Fault Diagnosis Interconnected systems Artificial neural networks Biological neural networks Fault diagnosis Neural Network Fault detection Takagi-Sugeno Model Neural networks Nonlinear Systems. Signal generators Takagi-Sugeno model Mathematical model
Content Type	Text
Resource Type	Article

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