摘要
基于数据的故障诊断方法凭借其优秀的工程适用性,已成为当前故障诊断领域的重点研究方向;但其算法模型的训练一般需要充足的样本数据,因此难以解决故障样本缺少的诊断问题.针对目标卫星无故障样本情况下的卫星姿态控制系统(ACS)故障诊断问题,提出一种基于DDC(deep domain confusion)迁移学习算法改进的故障定位技术.通过长短期记忆-自编码器(LSTM-AE)网络对标称卫星姿态信息重构并计算残差,再对其进行特征提取以训练BP网络故障定位分类器;同时引入DDC迁移学习算法,于分类器网络中添加域适应层并修改损失函数,学习目标卫星的健康和故障特征知识进而改进算法模型.最后通过三轴气浮台半物理仿真平台,验证了引入DDC迁移学习改进的故障定位技术的有效性.
The data-based fault diagnosis method has become a key research direction in the current fault diagnosis field due to its excellent engineering applicability;but the training of its algorithm model generally requires sufficient sample data,so it is difficult to solve the diagnosis problem lacking of fault samples.Aiming at the problem of satellite attitude control system(ACS)fault diagnosis when the target satellite has no fault samples,a fault location technology based on DDC(deep domain confusion)transfer learning algorithm improvement is proposed.Firstly,nominal satellite attitude information is reconstructed by the long-short term memory auto encoder(LSTM-AE)network and its residuals are calculated.Secondly,the features of extraction are trained into the BP network fault location classifier.Thirdly,the DDC transfer learning algorithm is introduced:a domain adaptation layer and modification of the loss function are added to classifier network to learn the health and fault characteristics of the target satellite and improve the algorithm model.Finally,through the semi-physical simulation platform of the three-axis air bearing table,the effectiveness of the fault location technology improved by the introduction of DDC migration learning is verified.
作者
王泽
程月华
宫江雷
郭小红
何漫丽
WANG Ze;CHENG Yuehua;GONG Jianglei;GUO Xiaohong;HE Manli(School of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China;Xi’an University of Electronic Science and Technology,Xi’an 710071,China;State Key Laboratory of Astronavigation Dynamics,Xi’an 710043,China)
出处
《空间控制技术与应用》
CSCD
北大核心
2022年第2期80-87,共8页
Aerospace Control and Application
基金
国家自然科学基金面上资助项目(61972398)。
