Toggle Main Menu Toggle Search

Open Access padlockePrints

Improved on-line process fault diagnosis using stacked neural networks

Lookup NU author(s): Dr Jie ZhangORCiD

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

Since it is generally difficult, if not impossible, to develop a perfect neural network, a single neural network can lack reliability. Therefore a single neural network based fault diagnosis system may not give reliable fault diagnosis. Neural network model reliability or robustness can be improved by combining several non-perfect neural networks. Each individual network is trained on a bootstrap re-sample of the original training data. The outputs from the individual networks are averaged to give the final diagnosis results. Applications of the proposed method to a continuous stirred tank reactor demonstrate that a stacked neural network can give more reliable diagnosis than a single neural network.


Publication metadata

Author(s): Zhang J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Control Applications

Year of Conference: 2002

Pages: 689-694

ISSN: 1085-1992

Publisher: IEEE

URL: http://dx.doi.org/10.1109/CCA.2002.1038684

DOI: 10.1109/CCA.2002.1038684

Library holdings: Search Newcastle University Library for this item

ISBN: 0780373863


Share