数字孪生驱动的转子系统变工况难测点响应实时预测方法

doi:10.3901/JME.260373

机械工程学报 ›› 2026, Vol. 62 ›› Issue (7): 221-233.doi: 10.3901/JME.260373

• 机械动力学 • 上一篇

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数字孪生驱动的转子系统变工况难测点响应实时预测方法

罗忠^1,2,3, 李洪雨^1,2, 李雷^1,2,3,4, 董胤哲^1,2

1. 东北大学机械工程与自动化学院沈阳 110819;
2. 东北大学航空动力装备振动及控制教育部重点实验室沈阳 110819;
3. 东北大学佛山研究生创新学院佛山 528312;
4. 中国航空发动机集团有限公司沈阳发动机研究所沈阳 110015

收稿日期:2025-02-24 修回日期:2025-12-31 发布日期:2026-05-25
作者简介:罗忠(通信作者)，男，1978年出生，博士，教授，博士研究生导师。主要研究方向为转子动力学，动力学相似，数字孪生。E-mail:zhluo@mail.neu.edu.cn
李洪雨，男，1999年出生，硕士研究生。主要研究方向为转子动力学，转子系统数字孪生。E-mail:lihongyu00002@163.com
基金资助:
国家自然科学基金(12272089，92360305)、广东省基础与应用基础研究基金(2023A1515110557)、辽宁省自然科学基金联合基金(2023-BSBA-102)、辽宁省教育厅基本科研(JYTQN2023162)、辽宁省科学计划(2023JH1/10400068)和中央高校基本科研业务专项资金(N2403022)资助项目。

Real-time Prediction Method for Response of Difficult-to-measure Points of Rotor System Under Variable Working Conditions Driven by Digital Twin

LUO Zhong^1,2,3, LI Hongyu^1,2, LI Lei^1,2,3,4, DONG Yinzhe^1,2

1. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819;
2. Key Laboratory of Vibration and Control of Aero-Propulsion System of Ministry of Education, Northeastern University, Shenyang 110819;
3. Foshan Graduate School of Innovation, Northeastern University, Foshan 528312;
4. Shenyang Engine Research Institute, Aero Engine Corporation of China, Shenyang 110015

Received:2025-02-24 Revised:2025-12-31 Published:2026-05-25

摘要/Abstract

摘要： 针对不同工况下转子系统某些关键位置的振动响应难以通过传感器实时获取的问题，提出了一种数字孪生驱动的转子系统变工况难测点响应实时预测方法。首先，基于动力学模型对转子系统振动特性进行分析，建立基于遗传算法-BP神经网络的高精简化模型作为动力学模型的数据驱动镜像，可提高模型响应计算速度，确保数字孪生模型的实时性；其次，提出一种基于RBF代理模型的模型动态更新方法，结合遗传算法-BP神经网络实现模型实时高效迭代优化，解决了实测振动信号与机理模型的融合问题；最后，搭建数字孪生实验平台，开展不同工况下转子系统难测点振动响应实时预测实验，完成数字孪生响应预测方法的有效性验证。实验结果表明：该数字孪生模型能够实现对转子系统运行状态的实时映射，且能够较好地解决转子系统的难测点响应预测问题。

关键词: 转子系统, 数字孪生, BP神经网络, RBF代理模型, 响应预测

Abstract: Aiming at the problem that the vibration response of some key positions of the rotor system under different working conditions is difficult to obtain in real time through the sensor, a real-time prediction method for the response of the rotor system under variable working conditions driven by digital twin is proposed. Firstly, based on the dynamic model, the vibration characteristics of the rotor system are analyzed, and a high-precision simplified model based on genetic algorithm-BP neural network is established as the data-driven image of the dynamic model, which can improve the response calculation speed of the model and ensure the real-time performance of the digital twin model. Secondly, a model dynamic updating method based on RBF surrogate model is proposed. The genetic algorithm-BP neural network is used to realize the real-time and efficient iterative optimization of the model, which solves the fusion problem of the measured vibration signal and the mechanism model. Finally, the digital twin experimental platform is built to carry out the real-time prediction experiment of the vibration response of the difficult point of the rotor system under different working conditions, and the validity of the digital twin response prediction method is verified. The experimental results show that the digital twin model can realize the real-time mapping of the operating state of the rotor system, and can better solve the problem of difficult point response prediction of the rotor system.

Key words: rotor system, digital twin, BP neural network, RBF surrogate model, response prediction

中图分类号:

TH113

罗忠, 李洪雨, 李雷, 董胤哲. 数字孪生驱动的转子系统变工况难测点响应实时预测方法[J]. 机械工程学报, 2026, 62(7): 221-233.

LUO Zhong, LI Hongyu, LI Lei, DONG Yinzhe. Real-time Prediction Method for Response of Difficult-to-measure Points of Rotor System Under Variable Working Conditions Driven by Digital Twin[J]. Journal of Mechanical Engineering, 2026, 62(7): 221-233.

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参考文献

[1] HUANG Y,TAO J,SUN G,et al. A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis[J]. Energy,2023,270:126894.
[2] GRIEVES M W. Product lifecycle management:the new paradigm for enterprises[J]. International Journal of Product Development,2005,2(1-2):71-84.
[3] 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.
[4] TAO F,XIAO B,QI Q,et al. Digital twin modeling[J]. Journal of Manufacturing Systems,2022,64:372-389.
[5] 宋学官,来孝楠,何西旺, 等. 重大装备形性一体化数字孪生关键技术[J]. 机械工程学报,2022,58(10):298-325. SONG Xueguan,LAI Xiaonan,HE Xiwang,et al. Key technologies of shape-performance integrated digital twin for major equipment[J]. Journal of Mechanical Engineering,2022,58(10):298-325.
[6] WANG S,LAI X,HE X,et al. Building a trustworthy product-level shape-performance integrated digital twin with multifidelity surrogate model[J]. Journal of Mechanical Design,2022,144(3):031703.
[7] GUIVARCH D,MERMOZ E,MARINO Y,et al. Creation of helicopter dynamic systems digital twin using multibody simulations[J]. Cirp Annals,2019,68(1):133-136.
[8] WANG J,YE L,GAO R,et al. Digital Twin for rotating machinery fault diagnosis in smart manufacturing[J]. International Journal of Production Research,2019,57(12):3920-3934.
[9] MOI T,CIBICIK A,RØLVÅG T. Digital twin based condition monitoring of a knuckle boom crane:An experimental study[J]. Engineering Failure Analysis,2020,112:104517.
[10] BOUZID S,VIAROUGE P,CROS J. Real-time digital twin of a wound rotor induction machine based on finite element method[J]. Energies,2020,13(20):5413.
[11] TIAINEN T,MIETTINEN J,VIITALA R,et al. Digital twin and virtual sensor for a rotor system[J]. Annals of DAAAM & Proceedings,2019,30.
[12] DUAN J,MA T,ZHANG Q,et al. Design and application of digital twin system for the blade-rotor test rig[J]. Journal of Intelligent Manufacturing,2023,34(2):753-769.
[13] MAGARGLE R,JOHNSON L,MANDLOI P,et al. A simulation-based digital twin for model-driven health monitoring and predictive maintenance of an automotive braking system[C]//Modelica,2017:132-135.
[14] CHETAN M,YAO S,GRIFFITH D T. Multi-fidelity digital twin structural model for a sub-scale downwind wind turbine rotor blade[J]. Wind Energy,2021,24(12):1368-1387.
[15] LI L,MA H,WEI M,et al. Thermal power plant turbine rotor digital twin automation construction and monitoring system[J]. Mathematical Problems in Engineering,2022,2022(1):8527281.
[16] ABOUBI F Z,MAÏGA A,CROS J,et al. Experimental identification of a coupled-circuit model for the digital twin of a wound-rotor induction machine[J]. Energies,2024,17(8):1948.
[17] RITTO T G,ROCHINHA F A. Digital twin,physics-based model,and machine learning applied to damage detection in structures[J]. Mechanical Systems and Signal Processing,2021,155:107614.
[18] 刘达新,王科,刘振宇,等. 基于数据融合与知识推理的机器人装配单元数字孪生建模方法研究[J]. 机械工程学报,2024,60(5):36-50. LIU Daxin,WANG Ke,LIU Zhenyu,et al. Research on digital twin modeling method for robotic assembly cell based on data fusion and knowledge reasoning[J]. Journal of Mechanical Engineering,2024,60(5):36-50.
[19] SUN J,YAN Z,HAN Y,et al. Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective[J]. Reliability Engineering & System Safety,2023,238:109404.
[20] SHI H,SONG Z,BAI X,et al. A novel digital twin model for dynamical updating and real-time mapping of local defect extension in rolling bearings[J]. Mechanical Systems and Signal Processing,2023,193:110255.
[21] LIU S,LU Y,ZHENG P,et al. Adaptive reconstruction of digital twins for machining systems:A transfer learning approach[J]. Robotics and Computer-Integrated Manufacturing,2022,78:102390.
[22] 付洋,曹宏瑞,郜伟强,等. 数字孪生驱动的航空发动机涡轮盘剩余寿命预测[J]. 机械工程学报,2021,57(22):106-113. FU Yang,CAO Hongrui,GAO Weiqiang,et al. Digital twin driven remaining useful life prediction for aero-engine turbine discs[J]. Journal of Mechanical Engineering,2021,57(22):106-113.
[23] WANG M,FENG S,INCECIK A,et al. Structural fatigue life prediction considering model uncertainties through a novel digital twin-driven approach[J]. Computer Methods in Applied Mechanics and Engineering,2022,391:114512.
[24] YAN S,ZHONG X,SHAO H,et al. Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization[J]. Reliability Engineering & System Safety,2023,239:109522.
[25] XIA J,HUANG R,CHEN Z,et al. A novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis[J]. Reliability Engineering & System Safety,2023,240:109542.
[26] 杨亮亮,来孝楠,何西旺,等. 面向飞机机翼数字孪生的在线加速度积分方法[J]. 机械工程学报,2024,60(2):342-355. YANG Liangliang,LAI Xiaonan,HE Xiwang,et al. Real-time integration of acceleration for aircraft wing digital twin[J]. Journal of Mechanical Engineering,2024,60(02):342-355.
[27] WANG H,ZHENG J,XIANG J. Online bearing fault diagnosis using numerical simulation models and machine learning classifications[J]. Reliability Engineering & System Safety,2023,234:109142.
[28] 李宏坤,魏代同,陈玉刚,等. 数字孪生驱动的旋转叶盘振动状态全景感知方法[J]. 机械工程学报,2023,59(21):270-282. LI Hongkun,WEI Daitong,CHEN Yugang,et al. A full-field sensing method for vibration state of rotating bladed disks driven by digital twin[J]. Journal of Mechanical Engineering,2023,59(21):270-282.
[29] LU Y,LIU C,KEVIN I,et al. Digital twin-driven smart manufacturing:Connotation,reference model,applications and research issues[J]. Robotics and Computer-Integrated Manufacturing,2020,61:101837.
[30] LIU Q,LENG J,YAN D,et al. Digital twin-based designing of the configuration,motion,control,and optimization model of a flow-type smart manufacturing system[J]. Journal of Manufacturing Systems,2021,58:52-64.
[31] TÄHEMAA T,BONDARENKO Y. Digital twin based synchronised control and simulation of the industrial robotic cell using virtual reality[J]. Journal of Machine Engineering,2019,19(1):128-144.
[32] LIU C,JIANG P,JIANG W. Web-based digital twin modeling and remote control of cyber-physical production systems[J]. Robotics and Computer-Integrated Manufacturing,2020,64:101956.
[33] 刘超,蔚夺魁,梁春华,等. 面向航空发动机研制的数字孪生定义研究[J]. 航空发动机,2023,49(3):1-8. LIU Chao,WEI Duokui,LIANG Chunhua,et al. Research on the definition of digital twin for aeroengine development[J]. Aeroengine,2023,49(03):1-8.
[34] 向家伟. 数值模型驱动的传动系统故障个性化诊断原理[J]. 机械工程学报,2021,57(15):116-128. XIANG Jiawei. Numerical model driving personalized diagnosis principle for fault detection in mechanical transmission systems[J]. Journal of Mechanical Engineering,2021,57(15):116-128.
[35] 向家伟. 机械故障数值模拟驱动的生成式对抗网络及智能诊断原理[J]. 中国科学:技术科学,2021,51(03):341-355. XIANG Jiawei. Numerical simulation driving generative adversarial networks in association with the artificial intelligence diagnostic principle to detect mechanical faults[J]. Scientia Sinica Technologica,2021,51(03):341-355.
[36] 李雷,罗忠,何凤霞,等. 考虑变幂数的畸变动力学相似试验模型设计方法及试验研究[J]. 机械工程学报,2020,56(23):107-117. LI Lei,LUO Zhong,HE Fengxia,et al. Design method of distorted dynamics similitude test model considering Variable power and experimental study[J]. Journal of Mechanical Engineering,2020,56(23):107-117.
[37] LI L,LUO Z,HE F,et al. An improved partial similitude method for dynamic characteristic of rotor systems based on Levenberg-Marquardt method[J]. Mechanical Systems and Signal Processing,2022,165:108405.
[38] ZHANG C,CAO P,ZHU R,et al. Dynamic modeling and analysis of the spline joint-flexible coupling-rotor system with misalignment[J]. Journal of Sound and Vibration,2023,554:117696.
[39] 程小勇,陈果,李成刚,等. 航空发动机转子系统模拟支承设计与刚度计算[J]. 航空计算技术,2012,42(6):9-12. CHENG Xiaoyong,CHEN Guo,LI Chenggang,et al. Rotor support simulation design and equivalent stiffness computation for aero-engine[J]. Aeronautical Computing Technique,2012,42(6):9-12.
[40] SONG X,LV L,LI J,et al. An advanced and robust ensemble surrogate model:Extended adaptive hybrid functions[J]. Journal of Mechanical Design,2018,140(4):041402.

数字孪生驱动的转子系统变工况难测点响应实时预测方法

Real-time Prediction Method for Response of Difficult-to-measure Points of Rotor System Under Variable Working Conditions Driven by Digital Twin

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