面向结构损伤识别的数字孪生建模

doi:10.3901/JME.260129

机械工程学报 ›› 2026, Vol. 62 ›› Issue (4): 328-341.doi: 10.3901/JME.260129

• 交叉与前沿 • 上一篇

扫码分享

面向结构损伤识别的数字孪生建模

杨亮亮^1,2, 龚壮壮^1,2, 何西旺^1,2, 王沐晨^1,2, 闵强³, 阚子云^1,2, 宋学官^1,2

1. 大连理工大学机械工程学院大连 116024;
2. 大连理工大学高性能精密制造全国重点实验室大连 116024;
3. 飞行器数字敏捷设计全国重点实验室成都 610031

收稿日期:2025-02-16 修回日期:2025-08-30 发布日期:2026-04-02
作者简介:杨亮亮,男,1993年出生,博士研究生。主要研究方向为传感器最优布局、损伤识别。E-mail:liangzai5358@163.com
闵强(通信作者),男,1985年出生,硕士,高级工程师。主要研究方向为飞行器数字敏捷智能设计与结构平台多学科联合设计。E-mail:minqiang11@163.com
宋学官,男,1982年出生,博士,教授,博士研究生导师。主要研究方向为多学科耦合建模与优化设计、工业大数据挖掘及数据驱动的预测技术、装备/智能化与数字孪生。E-mail:sxg@dlut.edu.cn
基金资助:
国家重点研发计划(2024YFB4709600)、国家自然科学基金(52205062)、江苏省自然科学基金(BK20220950)、长三角科技创新共同体联合攻关计划(2024C04056(CSJ))、江苏省前沿引领技术基础研究重大(BK20232031)、国家市场监督管理总局重点实验室(高参数电梯智能运维)(JSTJ-IOMHL-202503)、智控实验室开放基金(ICL-2023-0305)、国防科技大学装备状态感知与敏捷保障全国重点实验室基金(6142003202415)、上海航天科技创新基金(SAST2024-055)和民用航天预研(D020110)资助项目。

Digital Twin Construction for Structural Damage Identification

YANG Liangliang^1,2, GONG Zhuangzhuang^1,2, HE Xiwang^1,2, WANG Muchen^1,2, MIN Qiang³, KAN Ziyun^1,2, SONG Xueguan^1,2

1. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024;
2. State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, Dalian 116024;
3. National Key Laboratory of Digital and Agile Aircraft Design, Chengdu 610031

Received:2025-02-16 Revised:2025-08-30 Published:2026-04-02

摘要/Abstract

摘要： 损伤是结构的主要失效性形式，如何快速准确识别损伤可有效避免因损伤导致的失效和损坏，是结构健康监测的关键问题之一。鉴于此，本研究以提高结构损伤识别效率和准确性为目标，通过融合机理模型的模态信息和传感器的实测数据，提出一种面向结构损伤识别的数字孪生建模方法。首先，以简化的变截面类悬臂梁结构为例，依据结构几何尺寸、材料属性等信息构建结构孪生机理模型，基于Euler-Bernoulli理论快速获取健康结构固有频率。其次，依据机理模型计算的固有频率设置传感器采样频率和滤波参数，对监测数据进行分解、滤波和变换，提取结构损伤特征，并确定对损伤特征影响最大的损伤几何参数，结合损伤特征识别裂纹损伤长度和位置。然后，通过数值案例验证机理模型、损伤特征提取和数据驱动模型的可行性，结果表明，所提方法不仅可以提高损伤识别精度，而且能够快速识别损伤位置。最后，基于传感器实测数据构建面向结构损伤识别的数字孪生，在孪生空间中识别结构损伤，进一步验证提出孪生模型对结构损伤识别的有效性。该研究不仅为结构的损伤识别提供新的思路和方案，也为基于数字孪生的预测性维护提供新的借鉴和参考。

关键词: 数字孪生, 损伤识别, 结构损伤, 结构健康监测, 预测性维护

Abstract: Damage is the main form of structural failure, and how to quickly and accurately identify damage can effectively avoid failure and fault caused by damages, which is one of the key issues in structural health monitoring. In this study, to improve the accuracy of structural damage identification and achieve rapid damage identification, a digital twin(DT) construction method for structural damage identification is proposed by integrating mode information from mechanism model with sensor measurement data. First, taking the simplified cantilever beam with variable cross-section as an example, an DT mechanism model is built based on structural geometric dimensions, material properties, and so on. The natural frequencies of the health structure are quickly obtained using the Euler-Bernoulli theory. Subsequently, the sensor sampling frequency is set based on the calculated natural frequencies, and the monitored data is decomposed, filtered, and transformed to extract damage features. The most sensitive crack parameter type to damage features is obtained, and the crack length and position are identified by combining the damage features. Then, the feasibility of the mechanism model, damage feature extraction, and damage prediction model is verified through numerical cases. The results illustrate that the proposed method can effectively improve the accuracy of crack identification and quickly identify crack location. Finally, an DT is constructed based on sensor data to identify structural damage in the digital space, which further demonstrates the effectiveness of the proposed digital model for structural damage identification. This study not only provides new method and solution for structural damage identification, but also offers a new reference and guidance for predictive maintenance based on DTs.

Key words: digital twin, damage identification, structure damage, structural health monitoring, predictive maintenance

中图分类号:

HT17

杨亮亮, 龚壮壮, 何西旺, 王沐晨, 闵强, 阚子云, 宋学官. 面向结构损伤识别的数字孪生建模[J]. 机械工程学报, 2026, 62(4): 328-341.

YANG Liangliang, GONG Zhuangzhuang, HE Xiwang, WANG Muchen, MIN Qiang, KAN Ziyun, SONG Xueguan. Digital Twin Construction for Structural Damage Identification[J]. Journal of Mechanical Engineering, 2026, 62(4): 328-341.

参考文献

[1] ZHANG G，LIU Y，LIU J，et al. Causes and statistical characteristics of bridge failure：A review[J]. Journal of Traffic and Transportation Engineering，2022，9(3)：388-406.
[2] CHOWDHURY T S，MOHSIN F T，TONNI M M，et al. A critical review on gas turbine cooling performance and failure analysis of turbine blades[J]. International Journal of Thermofluids，2023，18：100329
[3] GUO J，LIU C，CAO J，et al. Damage identification of wind turbine blades with deep convolutional neural networks[J]. Renewable Energy，2021，174：122-133.
[4] GARDNER P，BULL L A，GOSLIGA J，et al. A population-based SHM methodology for heterogeneous structures：Transferring damage localisation knowledge between different aircraft wings[J]. Mechanical Systems and Signal Processing，2022，172：108918.
[5] SUN S，WANG T，YANG H，et al. Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function[J]. Renewable Energy，2022，181：59-70.
[6] 杜昌骏，张静，杨栋，等. 车致振动响应递归量化分析在桥梁结构损伤识别中的应用[J]. 振动工程学报，2024，37(7)：1098-1106. DU Changjun，ZHANG Jing，YANG dong，et al. Bridge damage identification using recurrence quantification analysis of vehicle-induced vibration response[J]. Journal of Vibration Engineering，2024，37(7)：1098-1106.
[7] CHADY T，OKARMA K，MIKOLAJCZYK R，et al. Extended damage detection and identification in aircraft structure based on multifrequency eddy current method and mutual image similarity assessment[J]. Materials，2021，14(16)：4452.
[8] ZHU Y，LI F，BAO W，et al. Fatigue crack detection under the vibration condition based on ultrasonic guided waves[J]. Structural Health Monitoring，2021，20(3)：931-941.
[9] BANSAL A，VETTIVEL S C，KUMAR M，et al. Weld defect identification and characterization in radiographic images using deep learning：Review[J]. Engineering Research Express，2023，5(2)：025079.
[10] LI Z，LIU X，LU H，et al. Surface crack detection in precasted slab track in high-speed rail via infrared thermography[J]. Materials，2020，13(21)：4837.
[11] CHEN L，CHEN W，WANG L，et al. Convolutional neural networks(CNNs)-based multi-category damage detection and recognition of high-speed rail (HSR) reinforced concrete (RC) bridge using test images[J]. Engineering Structures，2023，276：115306.
[12] CHAUPAL P，RAJENDRAN P. A review on recent developments in vibration-based damage identification methods for laminated composite structures：2010-2022[J]. Composite Structures，2023，311：116809.
[13] NIYIRORA R，JI W，MASENGESHO E，et al. Intelligent damage diagnosis in bridges using vibration-based monitoring approaches and machine learning：A systematic review[J]. Results in Engineering，2022，16：100761.
[14] MALEKLOO A，OZER E，ALHAMAYDEH M，et al. Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights[J]. Structural Health Monitoring，2022，21(4)：1906-1955.
[15] LI H，WANG T，WU G. Nonlinear vibration analysis of beam-like bridges with multiple breathing cracks under moving vehicle load[J]. Mechanical Systems and Signal Processing，2023，186：109866.
[16] NGUYEN T Q，VUONG L C，LE C M，et al. A data-driven approach based on wavelet analysis and deep learning for identification of multiple-cracked beam structures under moving load[J]. Measurement，2020，162：107862.
[17] 周宏元，张广才，王小娟，等. 基于改进蝴蝶优化算法的结构损伤识别[J]. 振动、测试与诊断，2023，43(1)：164-171，204. ZHOU Hongyuan，ZHANG Guangcai，WANG Xiaojuan，et al. Structural damage identification based on improved butterfly optimization algorithm[J]. Journal of Vibration，Measurement & Diagnosis，2023，43(1)：164-171，204.
[18] CHINKA S S B，PUTTI S R，ADAVI B K. Modal testing and evaluation of cracks on cantilever beam using mode shape curvatures and natural frequencies[J]. Structures，2021，32：1386-1397.
[19] MOUSAVI M，HOLLOWAY D，OLIVIER J C，et al. Beam damage detection using synchronisation of peaks in instantaneous frequency and amplitude of vibration data[J]. Measurement，2021，168：108297.
[20] FU Y，ZHANG Y，ZHOU H，et al. Timely online chatter detection in end milling process[J]. Mechanical Systems and Signal Processing，2016，75：668-688.
[21] 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(2)：342-355.
[22] 赵福斌，周轩，董雷霆. 基于数字孪生的飞机蒙皮裂纹智能检查维修策略[J]. 固体力学学报，2021，42(3)：277-286. ZHAO Fubin，ZHOU Xuan，DONG Leiting. An intelligent digital-twin-based strategy for the inspection and repair of aircraft skin cracks[J]. Chines Journal of Solid Mechanics，2021，42(3)：277-286.
[23] 董雷霆，周轩，赵福斌，等. 飞机结构数字孪生关键建模仿真技术[J]. 航空学报，2021，42(3)：113-141. DONG Leiting，ZHOU Xuan，ZHAO Fubin，et al. Key technologies for modeling and simulation of airframe digital twin[J]. Acta Aeronautica et Astronautica Sinica，2021，42(3)：113-141.
[24] 石健，刘冬，王少萍. 基于数字孪生的机电液系统PHM关键技术综述[J]. 机械工程学报，2024，60(4)：66-81. SHI Jian，LIU Dong，WANG Shaoping. Digital twin for complex electromechanical-hydraulic system PHM：A review[J]. Journal of Mechanical Engineering，2024，60(4)：66-81.
[25] 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.
[26] KAPTEYN M G，KNEZEVIC D J，WILLCOX K E. Toward predictive digital twins via component-based reduced-order models and interpretable machine learning[C]//AIAA Scitech 2020 Forum. Orlando，USA，2020：0418.
[27] MOUSAVI Z，VARAHRAM S，ETTEFAGH M M，et al. A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach[J]. Ocean Engineering，2024，292：116563.
[28] KIM W，YOUN B D. Physics-based digital twin updating and twin-based explainable crack identification of mechanical lap joint[J]. Reliability Engineering and System Safety，2025，253：110515.
[29] TENG S，CHEN X，CHEN G，et al. Structural damage detection based on transfer learning strategy using digital twins of bridges[J]. Mechanical Systems and Signal Processing，2023，191：110160.
[30] WANG L，LIU H，CHEN Z，et al. Combined digital twin and hierarchical deep learning approach for intelligent damage identification in cable dome structure[J]. Engineering Structures，2023，274：115172.
[31] YANG L，HE X，ZHANG C，et al. Crack identification driven by the fusion of mechanism and data for the variable-cross-section cantilever beam[J]. Mechanical Systems and Signal Processing，2023，196：110320.
[32] LUO J，ZHU S，ZHAI W. Exact closed-form solution for free vibration of Euler-Bernoulli and Timoshenko beams with intermediate elastic supports[J]. International Journal of Mechanical Sciences，2022，213：106842.
[33] ROVERI N，CARCATERRA A. Damage detection in structures under traveling loads by Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing，2012，28：128-144.

面向结构损伤识别的数字孪生建模

Digital Twin Construction for Structural Damage Identification

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	刘素艳, 董一林, 乔一鸣, 马增强. 基于改进蜣螂优化算法的轴承数字孪生模型的动态故障注入研究[J]. 机械工程学报, 2026, 62(3): 446-457.
[2]	王世博, 葛世荣, 管增伦, 周士林, 王丽杰, 李雪峰, 王赟, 袁晓明, 马广军. 面向数字孪生的采煤机滚筒实时截割载荷模型研究[J]. 机械工程学报, 2026, 62(3): 479-491.
[3]	李从昱, 高飞, 刘璐, 李弈林, 林京. 基于超声导波谱估计的频散曲线重构与薄壁结构损伤定量评估方法[J]. 机械工程学报, 2026, 62(2): 104-114.
[4]	刘海波, 邓平, 迟庆宇, 刘天然, 刘阔, 李特, 黄祖广, 刘行健, 薄其乐, STEVEN Y LIANG, 王永青. 智能加工中数据使能技术与应用[J]. 机械工程学报, 2026, 62(2): 407-444.
[5]	朱海华, 付泰然, 李霏, 刘长春, 蔡祺祥, 唐敦兵. 基于数字孪生的复杂产品装配物料过程齐套时间预测[J]. 机械工程学报, 2025, 61(8): 384-398.
[6]	李睿智, 陈悦敏, 闫纪红. 面向轨迹动态感知与自主决策的工业机器人数字孪生建模方法研究[J]. 机械工程学报, 2025, 61(7): 269-283.
[7]	胡炳涛, 钟锐锐, 冯毅雄, 杨晨, 王天跃, 洪兆溪, 谭建荣. 人-信息-物理互联环境下数字车间制造能力建模与自适应调度[J]. 机械工程学报, 2025, 61(3): 23-39.
[8]	马帅, 冷杰武, 陈祝云, 李巍华, 李波, 刘强. 基于数字孪生和深度迁移学习的电主轴热误差建模方法[J]. 机械工程学报, 2025, 61(3): 52-66.
[9]	丁梦珂, 高东岳, 顾海洋, 王博睿, 周国泰, 武湛君. 基于BO-CNN模型的复合材料疲劳损伤超声导波监测方法[J]. 机械工程学报, 2025, 61(24): 38-47.
[10]	王玉静, 李祎然, 康守强, 刘连胜, 李玉庆, 孙宇林. 基于数字孪生的不同工况下谐波减速器故障诊断方法[J]. 机械工程学报, 2025, 61(18): 12-26.
[11]	王柏村, 宋词, 苑艺修, 周慧颖, 鲍劲松, 黄思翰, 刘蔚然, 刘庭煜, 阮兵, 陶飞, 谢海波, 杨华勇. 面向人本智造的人体运动数字孪生研究与应用进展[J]. 机械工程学报, 2025, 61(15): 21-39.
[12]	黄思翰, 彭志诚, 朱启章, 王柏村, 张明睿, 马妮, 冷杰武, 郑湃, 敬石开, 王国新, 阎艳. 面向工业元宇宙的人本智造系统数字孪生建模与分布式虚拟协作方法[J]. 机械工程学报, 2025, 61(15): 385-398.
[13]	徐晓强, 谭志文, 胡宏伟, 向昊南. 基于导波的板状结构多损伤监测方法研究[J]. 机械工程学报, 2025, 61(14): 130-141.
[14]	安友军, 刘成, 陈晓慧, 高开周, 董元发, 孟荣华. 具有设备动态性和加工速度选择的生产与维护集成调度研究[J]. 机械工程学报, 2025, 61(14): 362-382.
[15]	袁堂晓, 吴亲亲, 柳林燕, 汪惠芬, 田双. 基于数字孪生的生产安全风险实时评估方法[J]. 机械工程学报, 2025, 61(12): 315-326.