NDLI: Fault diagnosis using hybrid wavelet/ Elman neural networks

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Fault diagnosis using hybrid wavelet/ Elman neural networks

Content Provider	IEEE Xplore Digital Library
Author	Mirea, L.
Copyright Year	2011
Description	Author affiliation: “Gh. Asachi” Technical University of Iasi, Department of Automatic Control and Industrial Informatics, Blvd. D. Mangeron 53A, RO-700050, Romania (Mirea, L.)
Abstract	The paper addresses the problem of process fault diagnosis using a new kind of neural network, namely the hybrid wavelet/ Elman neural network. Hybrid learning based on a subclustering algorithm and the steepest-descent method is used to train the proposed neural network. The experiments refer to the sensor and actuator fault diagnosis of a sub-system from the evaporation station of the Lublin sugar factory, namely the evaporator. A generalised neural network based observer scheme is used to generate the residuals (symptoms) in the form of one step-ahead prediction errors. These are then classified by a Multi-Layer Perceptron neural network in order to take the appropriate decision regarding the type of the behaviour of the process (normal or abnormal).
Starting Page	1
Ending Page	6
File Size	184475
Page Count	6
File Format	PDF
ISBN	9781457711732
e-ISBN	9789736213212
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-14
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	ICSTCC
Subject Keyword	Fault diagnosis Context Neurons Vectors Production facilities Sugar Biological neural networks
Content Type	Text
Resource Type	Article

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