Application of Immune Genetic Neural Network in Pump-jack Fault Diagnosis

Author

Ren, Weijian ; Liu, Dan

Author_Institution

Post Doctorial Res. Center of Control Sci. & Eng., Univ. of Sci. & Technol., Beijing, China

Volume

4

fYear

2009

fDate

19-21 May 2009

Firstpage

384

Lastpage

388

Abstract

A new RBF neural network is presented to overcome the shortcoming that the training process of RBF neural network is slow in the paper. The immune genetic algorithm is combined with the RBF neural network to optimize the center of the RBF network, improve the definition method of affinity degree, and introduce adjusting factor based on density. Thus the learning efficiency and approximation precision are improved, and the number of constructing the centers of hide layer of the network is dispensable. The algorithm was successful in fault diagnosis of pump-jack.

Keywords

approximation theory; artificial immune systems; fault diagnosis; genetic algorithms; learning (artificial intelligence); lifting equipment; mining equipment; oil drilling; pumps; radial basis function networks; RBF neural network; approximation precision; immune genetic neural network; learning efficiency; oil mining mechanical device; optimization; pump-jack fault diagnosis; Artificial intelligence; Artificial neural networks; Fault diagnosis; Feedforward neural networks; Genetic algorithms; Intelligent systems; Neural networks; Petroleum; Pumps; Radial basis function networks; RBF neural network; fault diagnosis; immune genetic algorithm; pump-jack;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2009. GCIS '09. WRI Global Congress on

Conference_Location

Xiamen

Print_ISBN

978-0-7695-3571-5

Type

conf

DOI

10.1109/GCIS.2009.48

Filename

5209276