Fault Diagnosis Research for Servo Valve Based on GA-BP Neural Network

被引：10

作者：

Zheng, Feilong ^{[1
]}

Zeng, Liangcai ^{[1
]}

Lu, Yundan ^{[1
]}

Kai, Gangsheng ^{[2
]}

Fu, Shuguang ^{[3
]}

机构：

[1] Wuhan Univ Sci & Technol, Minist Educ, Key Lab Met Equipment & Control Technol, Wuhan 430081, Peoples R China

[2] WISCO, Equipment Dept, Wuhan 430083, Peoples R China

[3] Beijing Inst Space Launch Technol, Beijing 100076, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE | 2015年 / 12卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Fault Diagnosis; Neural Network; Genetic Algorithm; Electro-Hydraulic Servo Valve;

D O I：

10.1166/jctn.2015.4188

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

In view of the defects of BP neural network, this paper puts forward a GA-BP neural network fault diagnosis approach which uses genetic algorithm to optimize the network structure, weights and thresholds, and applies it to diagnose the fault of electro-hydraulic servo valve. This paper selects the flow characteristic curves which represent four states of electro-hydraulic servo valve to train the network, and a certain GA-BP network with great generalization ability is obtained. The verification shows that this approach's structure is simple; fault identification speed is fast and has great significance on establishing the electro-hydraulic servo valve fault diagnosis system.

引用

页码：2846 / 2850

页数：5

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