Research of RBF Neural Networks Algorithm to Fault Diagnosis of Rotary Machinery

Author

Wang Xiao-yue ; Zhang Zhong-kui

Author_Institution

Dept. of the Libr., Shandong Univ. of Technol., Zibo, China

fYear

2009

fDate

11-12 July 2009

Firstpage

331

Lastpage

334

Abstract

In order to overcome the problems of slow rate of convergence, falling easily into local minimum, instability learning performance caused by initial value in BP algorithm, a new diagnosis method based on RBF neural networks was proposed. And the diagnosis method is applied to rotary machinery fault diagnosis. The result shows that the RBF network has very high learning convergence speed and better classifying performance. RBF network has good practicality in the field of equipment fault diagnosis.

Keywords

convergence; fault diagnosis; learning (artificial intelligence); machinery; pattern classification; radial basis function networks; RBF neural network algorithm; classifying performance; fault diagnosis; learning convergence speed; radial basis function network; rotary machinery; Artificial neural networks; Clustering algorithms; Convergence; Fault diagnosis; Iterative algorithms; Machinery; Neural networks; Radial basis function networks; Space technology; Vectors; RBF neural networks; fault diagnosis; rotary machinery;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on

Conference_Location

Zhangjiajie

Print_ISBN

978-0-7695-3728-3

Type

conf

DOI

10.1109/CASE.2009.118

Filename

5194458