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Vol. 169, Issue 4, April 2014, pp. 101-106

 

Bullet

 

Fault Diagnosis of Hydraulic Servo Valve Based on Genetic Optimization
RBF-BP Neural Network
 

1 Li-Ping FAN, 2 Chun-Yan LIU, 3 Yi LIU

1 College of Environment and Safety Engineering, Shenyang University of Chemical Technology, Shenyang, Liaoning 110142, China
2 College of information Engineering, Shenyang University of Chemical Technology, Shenyang, Liaoning 110142, China
3 Branch Company of Rolling Equipment, North Heavy Industry Group Co., Ltd., Shenyang, Liaoning 110141, China
1 Tel.: 86-24-89385088, fax: 86-24-89385088

1 E-mail: flpsd@163.com

 

Received: 13 February 2014 /Accepted: 7 April 2014 /Published: 30 April 2014

Digital Sensors and Sensor Sysstems

 

Abstract: Electro-hydraulic servo valves are core components of the hydraulic servo system of rolling mills. It is necessary to adopt an effective fault diagnosis method to keep the hydraulic servo valve in a good work state. In this paper, RBF and BP neural network are integrated effectively to build a double hidden layers RBF-BP neural network for fault diagnosis. In the process of training the neural network, genetic algorithm (GA) is used to initialize and optimize the connection weights and thresholds of the network. Several typical fault states are detected by the constructed GA-optimized fault diagnosis scheme. Simulation results shown that the proposed fault diagnosis scheme can give satisfactory effect.

 

Keywords: Fault diagnosis, RBF-BP neural network, Genetic algorithm, Servo valve.

 

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