A rapid response intelligent diagnosis network using radial basis function network

Authors:
Guangrui Wen;Liangsheng Qu;Xining Zhang
Affiliations:
Research Institute of Diagnostics & Cybernetics, College of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China;Research Institute of Diagnostics & Cybernetics, College of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China;Research Institute of Diagnostics & Cybernetics, College of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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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.