摘要
针对卫星姿态控制系统执行器机构故障问题,提出了一种基于循环神经网络的故障诊断方法。对卫星姿态控制系统建模,进行故障分析并采集星敏感器和角速度陀螺的连续时刻故障数据。设计六种异构的循环神经网络,对故障数据进行故障诊断和分类,分别从网络深度、反馈单元、激活函数和训练算法对比网络效果。带有门循环单元的(gate recurrent unit,GRU)深层循环神经网络训练效果更好,其故障诊断准确率达到了95.7%。结果表明对于时序的卫星数据,门循环单元和带有一定深度的循环神经网络故障诊断效果更优。
To solve the problem of actuator failure in satellite attitude control system,a fault diagnosis method based on recurrent neural network was proposed.The satellite attitude control system was modeled,fault analysis was carried out,continuous time fault data of star sensor and angular velocity gyro were collected.Six kinds of heterogeneous cyclic neural networks were designed to diagnose and classify the fault data,and the network effect was compared in terms of the network depth,feedback unit,activation function and training algorithm.The effect of deep loop neural network with GRU is better,the accuracy of fault diagnosis is 95.7%.The results show that,for time series satellite data,GRU and the recurrent neural network with a certain depth have better fault diagnosis effect.
作者
倪平
闻新
NI Ping;WEN Xin(School of Astronautics,Shenyang Aerospace University,Shenyang 110136,China;Academy of Astronautics,Nanjing University of Aeronautics&Astronautics,Nanjing 210016,China)
出处
《中国空间科学技术》
CSCD
北大核心
2021年第4期121-126,共6页
Chinese Space Science and Technology
