数字孪生驱动的反射面天线运行状态监测与虚拟调试方法

doi:10.3901/JME.260243

机械工程学报 ›› 2026, Vol. 62 ›› Issue (5): 263-273.doi: 10.3901/JME.260243

• 机械动力学 • 上一篇

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数字孪生驱动的反射面天线运行状态监测与虚拟调试方法

吴锦辉^1,2, 陈永昶^1,2, 吴亚男^1,2, 郝会乾³, 袁翔⁴, 陶友瑞^1,2

1. 河北工业大学省部共建电工装备可靠性与智能化国家重点实验室天津 300401;
2. 河北工业大学机械工程学院天津 300401;
3. 中国电子科技集团公司第五十四研究所石家庄 050000;
4. 沧州热力有限公司沧州 061001

收稿日期:2024-06-13 修回日期:2024-12-12 发布日期:2026-04-23
作者简介:吴锦辉(通信作者),男,1991年出生,博士,副教授,硕士研究生导师。主要研究方向为机器人可靠性分析与优化设计、超大口径天线数字孪生建模与智能运维、机电耦合动态特性分析与可靠性设计。E-mail:wujinhui@hebut.edu.cn
陈永昶,男,2000年出生,硕士研究生。主要研究方向为反射面天线数字孪生建模、机电耦合动态特性分析。E-mail:202221202131@stu.hebut.edu.cn
基金资助:
国家自然科学基金资助项目(U23A6017)。

A Digital Twin-driven Approach for Operational Status Monitoring and Virtual Debugging of Reflector Antennas

WU Jinhui^1,2, CHEN Yongchang^1,2, WU Yanan^1,2, HAO Huiqian³, YUAN Xiang⁴, TAO Yourui^1,2

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401;
2. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401;
3. The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050000;
4. Cangzhou Thermal Power Co., Ltd., Cangzhou 061001

Received:2024-06-13 Revised:2024-12-12 Published:2026-04-23

摘要/Abstract

摘要： 为实现对反射面天线的可视化监控与虚拟调试，提出一种基于控制-电气-机械多领域统一建模和模型参数识别的反射面天线高保真数字孪生系统构建方法。首先，分别构建反射面天线方位与俯仰关节的控制、电气及机械各领域模型，并通过信息传递和能量转换规律将三者耦合为多领域统一模型。其次，在搭建天线物理实体运动控制、数据采集系统与Unity虚拟监控系统的基础上，实时监测天线物理实体运行状态并将运动数据实时采集到上位机，辨识天线机械系统模型的动力学参数，并更新控制与电机模型参数，使多领域统一数字孪生模型可以实时、准确地映射天线物理实体的运动状态。最后，基于多领域统一数字孪生模型进行反射面天线运动性能虚拟调试，并以0.9 m反射面天线为试验对象，验证所建立模型方法的有效性与实用性。

关键词: 反射面天线, 数字孪生系统, 机电耦合动力学模型, 参数辨识, 状态监测, 虚拟调试

Abstract: To achieve visual monitoring and virtual debugging of reflector antennas, a high-fidelity digital twin system construction method is proposed based on the multi-domain unified modeling of the mechanical, electrical and control systems, and model parameter identification. First, the electrical model, machine model and control model of the azimuth and pitch joints are respectively constructed, and then coupled to build the multi-domain unified model according to information transmission and energy conversion laws. Then, the motion control system, data acquisition system and the Unity virtual monitoring system of the antenna physical entity are established to monitor operational status of the antenna physical entity in real-time, and collect the motion data and send to the upper computer in real-time. The dynamic parameters of the antenna's mechanical system are identified based on the motion data, and the parameters of the control and motor models are also updated, so that the real-time motion status of the physical antenna is accurately mapped by the multi-domain unified digital twin model. Finally, the motion performance of reflector antenna is virtually debugged based on the multi-domain unified digital twin model on a 0.9

Key words: reflector antennas, digital twin system, electromechanical coupling dynamic models, parameter identification, status monitoring, virtual debugging

中图分类号:

TG156

吴锦辉, 陈永昶, 吴亚男, 郝会乾, 袁翔, 陶友瑞. 数字孪生驱动的反射面天线运行状态监测与虚拟调试方法[J]. 机械工程学报, 2026, 62(5): 263-273.

WU Jinhui, CHEN Yongchang, WU Yanan, HAO Huiqian, YUAN Xiang, TAO Yourui. A Digital Twin-driven Approach for Operational Status Monitoring and Virtual Debugging of Reflector Antennas[J]. Journal of Mechanical Engineering, 2026, 62(5): 263-273.

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参考文献

[1] LIAN P，WANG C，XUE S，et al. Future research trend for improving large reflector antenna service performance[J]. Engineering，2021，7(8):1047-1050.
[2] LU J，YU J，ZHU L. On the multi-resonant antennas: Theory, history, and new development[J]. International Journal of RF and Microwave Computer-Aided Engineering，2019，29(9):e21808.
[3] Teng F，Wan J，Liu J. Review of terahertz antenna technology for science missions in space[J]. IEEE Aerospace and Electronic Systems Magazine，2023，38(2):16-32.
[4] Liu M，Fang S，Dong H，et al. Review of digital twin about concepts, technologies, and industrial applications[J]. Journal of Manufacturing Systems，2021，58:346-361.
[5] Wei Y，Hu T，Dong L，et al. Digital twin-driven manufacturing equipment development[J]. Robotics and Computer-Integrated Manufacturing，2023，83:102557.
[6] Schluse M，Priggemeyer M，Atorf L，et al. Experientable digital twins streamlining simulation based systems engineering for industry 4.0[J]. IEEE Transactions on Industrial Informatics，2018，14(4):1722-1731.
[7] 陶飞，刘蔚然，张萌，等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统，2019，25(1):1-18. TAO Fei，LIU Weiran，ZHANG Meng，et al. Five- dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems，2019，25(1):1-18.
[8] Wang J，Ye L，Gao X，et al. Digital Twin for rotating machinery fault diagnosis in smart manufacturing[J]. International Journal of Production Research，2019，57(12):3920-3934.
[9] 王泽民，郝亮亮，鲁秦怡，等. 面向发电机进相深度极限与安全边界量化分析的数字孪生模型[J]. 中国电机工程学报，2024，2:1-14. WANG Zemin，HAO Liangliang，LU Qinyi，et al. A digital twin model for quantitative analysis of the depth limit and safety boundary of generator leading phase operation[J]. Proceedings of the CSEE，2024，2:1-14.
[10] Arrano F，Konstantinou G. Modular design and real-time simulators toward power system digital twins implementation[J]. IEEE Transactions on Industrial Informatics，2023，19(1):52-61.
[11] Liu X，Gan H，Luo Y，et al. Digital-twin-based real-time optimization for a fractional order controller for industrial robots[J]. Fractal and Fractional，2023，7(2):167.
[12] LAI X，YANG L，HE X，et al. Digital twin-based structural health monitoring by combining measurement and computational data:An aircraft wing example[J]. Journal of Manufacturing Systems，2023，69:76-90.
[13] JIANG C，VEGA A，RAMANCHA K，et al. Bayesian calibration of multi-level model with unobservable distributed response and application to miter gates[J]. Mechanical Systems and Signal Processing，2022，170:108852.
[14] 杜莹莹，罗映，彭义兵，等. 基于数字孪生的工业机器人三维可视化监控[J]. 计算机集成制造系统，2023，29(6):2130-2138. DU Yingying，LUO Ying，PENG Yibing，et al. 3D visual monitoring system of industrial robot based on digital twin[J]. Computer Integrated Manufacturing Systems，2023，29(6):2130-2138.
[15] Zhang B，Zhang M，Dong T，et al. Design of digital twin system for DC contactor condition monitoring[J]. IEEE Transactions on Industry Applications，2023，59(4):3904-3909.
[16] ZHOU Y，TANG J，YIN X，et al. Digital twins visualization of large electromechanical equipment[J]. IEEE Journal of Radio Frequency Identification，2022，6:993-997.
[17] 傅贵武，王兴波，田英. 基于五轴加工中心智能生产线的数字孪生应用研究[J]. 工程设计学报，2021，28(04):426-432. FU Guiwu，WANG Xingbo，TIAN Ying. Research on application of digital twin based on intelligent production line of five-axis machining center[J]. Chinese Journal of Engineering Design，2021，28(04):426-432.
[18] YANG X，LEI Y，LIU H，et al. Rigid-flexible coupled modeling of compound multistage gear system considering flexibility of shaft and gear elastic deformation[J]. Mechanical Systems and Signal Processing，2023，200:110632.
[19] WANG J，NIU X，GAO X，et al. Digital twin-driven virtual commissioning of machine tool[J]. Robotics and Computer-Integrated Manufacturing，2023，81:102499.
[20] CLAUDIO G，MARCO C，ALEXANDER O，et al. Dynamic identification of the franka emika panda robot with retrieval of feasible parameters using penalty-based optimization[J]. IEEE Robotics and Automation Letters，2019，4(4):4147-4154.
[21] Sousa D，Cortesao R. Inertia tensor properties in robot dynamics identification:A linear matrix inequality approach[J]. IEEE/ASME Transactions on Mechatronics，2019，24(1):406-411.

数字孪生驱动的反射面天线运行状态监测与虚拟调试方法

A Digital Twin-driven Approach for Operational Status Monitoring and Virtual Debugging of Reflector Antennas

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