摘要
大数据、工业物联网、人工智能等使能技术的发展促进了数字孪生与高端装备运维的深度融合,使得传统的“定期修”“故障修”运维模式向“预防修”“状态修”智能运维模式的升级,成为高端装备智能运维领域的研究热点。数字孪生充分利用机理模型、实时传感数据、历史数据以及专家知识等信息,集成多学科、多变量、多层次、多尺度、多粒度、多概率的建模仿真过程,准确表征数据特征并进行高效精准的计算分析,实现虚实空间的高精度、高可靠、高可信的映射及演化,为实际物理系统的状态评估、故障预警与运维决策提供支持。对数字孪生技术在高端装备智能运维领域的发展现状、关键技术及工程应用等进行了梳理,并对未来的挑战与难点进行了总结展望。
The development of enabling technologies including big data,industrial Internet of things and artificial intelligence has promoted the deep integration of digital twins and high-end equipment operation and maintenance,which make the traditional regular-repair and failure-repair operation and maintenance mode upgrade to intelligent mode preventive-repair and state-repair,and has become a research hotspot in the field of intelligent operation and maintenance of high-end equipment.By fully using information such as mechanism models,real-time sensor data,historical data and expert knowledge and integrating modeling and simulation processes of multi-disciplinary,multi-variable,multi-level,multi-scale,multi-granularity and multi-probability,digital twin could accurately characterize data characteristics and perform efficient and accurate calculations,which achieved high-precision,high-reliability and high-credibility mapping and evolution of virtual and real space.It provided support for state assessment,fault warning and operation and maintenance decision-making of actual physical systems.The development status,key technologies and engineering applications of digital twin technology in high-end equipment intelligent operation and maintenance were reviewed,and the future challenges and difficulties were summarized.
作者
高士根
周敏
郑伟
张林鍹
张斌
宋海锋
吴兴堂
李妮
王昆玉
GAO Shigen;ZHOU Min;ZHENG Wei;ZHANG Linxuan;ZHANG Bin;SONG Haifeng;WU Xingtang;LI Ni;WANG Kunyu(State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China;National Research Center of Railway Safety Assessment,Beijing Jiaotong University,Beijing 100044,China;State Engineering Research Center of Computer Integrated Manufacturing Systems,Tsinghua University,Beijing 100084,China;Metals and Chemistry Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;School of Electronic Information Engineering,Beihang University,Beijing 100083,China;School of Automation Science and Electrical Engineering,Beihang University,Beijing 100083,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2022年第7期1953-1965,共13页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(61925302,61790573,62073027)。
关键词
数字孪生
高端装备
智能运维
故障诊断
故障预警
digital twin
advanced equipment
intelligent operation and maintenance
fault diagnosis
fault warning