ANN with the Error Contracting Gradually Algorithm and Its Application in Generator Fault Diagnosis

DOI

10.2991/iske.2007.50How to use a DOI?

Keywords

Neural network, The error contracting gradually algorithm, BP, Turbine-generator set vibration fault, Rotor winding inter-turn short circuit fault, Fault diagnosis

Abstract

Based on analysis of conventional back-propagation (BP) network, the causes of error curve oscillation and excessive learning are first proposed. Next, a new BP with the error contracting gradually algorithm is put forward, through setting up neuron error threshold function, only when neuron’s error is bigger than the error threshold, the neuron’s parameters can be adjusted, otherwise the neuron which error is smaller than the error threshold can’t be adjusted. The algorithm can avoid the excessive learning and learning error oscillation. Finally, two fault diagnosis models based on the new BP algorithm is set up respectively, which are turbine-generator set vibration fault diagnosis model and rotor winding inter-turn short circuit fault diagnosis model. The results of verification show that the model has faster speed and higher diagnosis precision

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shuting Wan
PY  - 2007/10
DA  - 2007/10
TI  - ANN with the Error Contracting Gradually Algorithm and Its Application in Generator Fault Diagnosis
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 294
EP  - 300
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.50
DO  - 10.2991/iske.2007.50
ID  - Wan2007/10
ER  -