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Improved on-line process fault diagnosis using stacked neural networks

Lookup NU author(s): Dr Jie ZhangORCiD

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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

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