Share Email Print
cover

Proceedings Paper

Rapid response neural network for rotor system diagnosis
Author(s): Guanghua Xu; Lin Jiang; Liangsheng Qu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

With the rapid growth of industrial application of ANN, an intelligent diagnostic system for a large rotor system based on probabilistic neural network is developed and introduced into practice. Due to its intelligence and rapid response without human interference, it is called rapid response neural net (RRN). In this paper, the principles of construction, net architecture, and feature selection are discussed. Minimum information loss in preprocessing net and correct architecture selection are emphasized in constructing a PNN 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 RRN are given. The practical effects in two chemical complexes are analyzed. Both of them indicate that RRN possesses good function.

Paper Details

Date Published: 28 August 1995
PDF: 6 pages
Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); doi: 10.1117/12.217507
Show Author Affiliations
Guanghua Xu, Xian Jiaotong Univ. (China)
Lin Jiang, Xian Jiaotong Univ. (China)
Liangsheng Qu, Xian Jiaotong Univ. (China)


Published in SPIE Proceedings Vol. 2620:
International Conference on Intelligent Manufacturing
Shuzi Yang; Ji Zhou; Cheng-Gang Li, Editor(s)

© SPIE. Terms of Use
Back to Top