基于分层神经网络的航天器故障诊断技术

为了提高卫星、飞船等复杂系统的故障诊断速度和精度，文章提出了一种基于分层神经网络的整星故障诊断模型。模型中的上层神经网络采用自组织特征映射网络，完成整星故障的初步定位与辨识；下层神经网络采用广义回归神经网络，实现整星各分系统故障的精确定位和定因。引入主元分析法实现原始状态变量的降维，减少神经网络神经元数量。该模型已成功应用于某卫星各分系统的故障诊断，提高了诊断效率，并能精确给出诊断结果。

Spacecraft fault diagnosis based on hierarchical neural network

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.