摘要
健康预测和数字孪生技术是实现小卫星数字化设计与在轨卫星健康管理的重要手段,它可以为小卫星数字化设计提供必要的支持,保证其安全、可靠地在轨服役运行。针对小卫星数字化程度低、各阶段数据流动少、运行环境复杂多变等特点,本文提出了基于数字孪生的小卫星全生命周期智能诊断与健康预测框架。首先,对健康预测、数字孪生和数字主线等技术及其在卫星领域的应用做了概述。其次,充分利用小卫星在轨和地面等各类数据,构建了面向小卫星健康预测的数字孪生模型,以及小卫星全生命周期的数字主线。最后,结合机理知识和性能数据,提出了基于信息融合的薄弱环节识别、智能诊断、健康评估和在轨剩余寿命预测方法。所提出框架可以有效地实现物理和虚拟数据融合、虚实互连和数据处理,为小卫星数字化设计与健康预测提供必要支撑。
Health prediction and digital twin are essential to autonomous control and health management of satellites in orbit.They support small satellites’autonomous control and ensure their safe and reliable in-orbit operation.For small satellites’characteristics of low digitalization,low data flows and complex and variable operation environments,this paper proposes a framework of intelligent diagnosis and health prediction for the whole life cycle of small satellites based on digital twin.Firstly,an overview of health prediction,digital twin,digital mainline and their applications in the satellite field is provided.Secondly,the digital twin models for small satellite health prediction and a digital mainline for the whole life cycle of small satellites are constructed by making full use of various data.Finally,combining with the mechanism and performance data,the weak process identification,intelligent diagnosis,health assessment and remaining life prediction methods based on information fusion are proposed.The proposed framework can effectively realize physical and virtual data fusion,virtual-real interconnection,and data processing to achieve accurate health prediction of small satellites.
作者
曹雪蕊
张学艺
彭开香
崔玉福
CAO Xuerui;ZHANG Xueyi;PENG Kaixiang;CUI Yufu(Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education,School of Automation,University of Science and Technology Beijing,Beijing 100083,China;DFH Satellite Co.,Ltd.,Beijing 100094,China)
出处
《南京航空航天大学学报》
CAS
CSCD
北大核心
2022年第S01期35-42,共8页
Journal of Nanjing University of Aeronautics & Astronautics
基金
国家重点研发计划(2021YFB3301200)
国家自然科学基金(61873024)。
关键词
数字孪生
数字主线
智能诊断
健康预测
digital twin
digital mainline
intelligent diagnosis
health prediction
