Application of BP Neural Network in Fault Diagnosis of FOG SINS

被引：0

作者：

Wu, Lei ^{[1
]}

Sun, Feng ^{[1
]}

Chen, Shitong ^{[1
]}

机构：

[1] Harbin Engn Univ, Harbin 150001, Peoples R China

来源：

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年

关键词：

BP neural network; FOG SINS; fault diagnosis;

D O I：

10.1109/WCICA.2008.4594408

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 BP (back-propagation) 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 BP 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. Finally the design steps of fault detection system based on neural network for FOG SINS are summarized.

引用

页码：9322 / 9326

页数：5