Application of RBF Neural Network in Fault Diagnosis of FOG SINS

被引：0
|
作者：
Wu Lei [1 ]
Sun Rong-Ping [1 ]
Cheng Jian-Hua [1 ]
机构：
[1] Harbin Engn Univ, Automat Coll, Harbin, Peoples R China
来源：
2008 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS, VOLS 1-4 | 2008年
关键词：
RBF neural network; FOG SINS; fault diagnosis;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Taking FOG SINS (fiber-optic gyroscope strapdown inertial system) as an object, a new fault diagnostic scheme based on RBF(radial basis function) neural network is proposed. Being capable of training and simulating data off-line, neural networks provide a solution to overcome some drawbacks of the quantitative fault diagnosis. The fault tree of FOG SINS is analyzed, which is the basis of the study of neural network fault diagnosis technology. The structure and inferential mechanism of RBF network used for elementary fault diagnosis are discussed in detail. Training simulation results of the neural network are given and an improved effect with real data is obtained, which show the feasibility of the proposed scheme.
引用
收藏
页码：912 / 915
页数：4
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