Abstract
An intelligent diagnostic system for a large rotor system based on radial basis function network, called rapid response intelligent diagnosis network (RRIDN), is proposed and introduced into practice. In this paper, the principles, model, net architecture, and fault feature selection of RRIDN are discussed in detail. Correct model architecture selection are emphasized in constructing a radial basis neural network of high performance. In order to reduce the amount of real training data, the counterexamples of real data are adopted. Some training and testing results of rapid response intelligent diagnosis networks are given. The practical effects in two chemical complexes are analyzed. Both of them indicate that RRIDN possesses good function.
This paper is sponsored by NSFC 50335030.
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Wen, G., Qu, L., Zhang, X. (2005). A Rapid Response Intelligent Diagnosis Network Using Radial Basis Function Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_82
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DOI: https://doi.org/10.1007/11427469_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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