航天器环境工程2013，Vol.30Issue(2)：203-208,6.DOI:10.3969/j.issn.1673-1379.2013.02.019
基于分层神经网络的航天器故障诊断技术
Spacecraft fault diagnosis based on hierarchical neural network
摘要
Abstract
For improving the diagnosis speed and accuracy of a large-scale and complex system like satellite or spaceship, a hierarchical diagnosis model of satellite is proposed. A self organizing feature mapping neural network is adopted for the upper network, which is responsible for the preliminary fault localization and identification for the whole satellite. Generalized regression neural network(GRNN) is adopted for the lower network, which is responsible for accurately determining the localization and causes of faults for each subsystem of the satellite. The principal component analysis(PCA) is introduced to reduce the dimension of the original state variables. So, the number of the upper neural network neurons is reduced. The method is successfully applied to the fault diagnosis for the subsystems of a satellite. The accurate diagnosis result is obtained with improved efficiency.关键词
航天器/故障诊断/分层神经网络/广义回归神经网络/自组织特征映射网络Key words
spacecraft fault diagnosis/ hierarchical neural network/ generalized regression neural network(GRNN)/ self organizing feature mapping network(SOFMN)分类
航空航天引用本文复制引用
安若铭,高阳..基于分层神经网络的航天器故障诊断技术[J].航天器环境工程,2013,30(2):203-208,6.基金项目
微小型航天器技术重点实验室基金及中央高校基本科研业务费专项(项目编号:HIT.KLOF.2010020) （项目编号:HIT.KLOF.2010020）
