基于1D-CNN的卫星姿态控制系统故障诊断方法  

Fault Diagnosis Method of Satellite Attitude Control System Based on 1D-CNN

作　　者：闻新[1] 龙弟之 王俊鸿 魏炳翌 Wen Xin;Long Dizhi;Wang Junhong;Wei Bingyi(Academy of Astronautics,Nanjing University of Aeronautics&Astronautics,Nanjing 210016,China;Beijing Aerospace Automatic Control Institute,China Academy of Launch Vehicle Technology,Beijing 100854,China)

机构地区：[1]南京航空航天大学航天学院,南京210016 [2]中国运载火箭技术研究院北京航天自动控制研究所,北京100854

出　　处：《兵工自动化》2020年第7期1-6,共6页Ordnance Industry Automation

摘　　要：为解决卫星姿态控制系统中自主故障检测和诊断的问题,提出一种改进的1D-CNN卫星姿态控制系统故障诊断方法。以卫星姿态控制系统的故障诊断为背景,构建航天器姿态动力学模型,将卷积神经网络(convolutional neural network,CNN)与快速卷积算法相结合,对卷积神经网络的拓扑结构进行改进,根据BP算法,将1维原始数据作为输入,结合反作用飞轮作为执行机构的技术特征,给出一种基于卷积神经网络的故障检测和隔离方法。仿真结果验证了该方法对卫星姿态控制系统实时故障检测和分类的有效性。To solve the problem of autonomous fault detection and diagnosis in satellite attitude control system,an improved one-dimensional convolution neural network fault diagnosis method is proposed.Based on the fault diagnosis of satellite attitude control system,the attitude dynamics model of spacecraft is constructed.The convolutional neural network(CNN)is integrated with fast convolution algorithm,and the topology of convolutional neural network is improved.According to BP algorithm,a fault detection and isolation method based on convolution neural network is proposed,which takes one-dimensional raw data as input and combines the technical characteristics of reaction flywheel as actuator.The simulation results verify the validity of this method for real-time fault detection and classification of satellite attitude control system.

关 键 词：故障诊断 卷积神经网络 航天器姿态控制系统 反作用飞轮 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]