面向结构静力试验监测的高精度数字孪生方法

Abstract

Abstract:

Experimental validation and numerical simulation are two typical methods for evaluating the structural strength. However， the experimental validation method based on sparse sensors is difficult to ensure the coverage of the structural stress monitoring， and the numerical simulation method may lead to the insufficient accuracy of stress results due to the simplification and idealization of physical entities. Therefore， it is a challenging issue to comprehensively utilize the advantages of these two methods of strength evaluation and carry out data fusion to achieve the full-field structural stress monitoring. In this study， the Digital Twin for Structural Static Test Monitoring （DT-SSTM） method is proposed， which can obtain a high-precision digital twin model of structural static test to realize the real-time monitoring of the structural stress fields and the structural strength evaluation. The DT-SSTM method includes two stages： offline and online stages. In the offline stage， the Gradient Boosting Decision Tree （GBDT） algorithm is used to train the simulation data and build a pre-trained model. In the online stage， based on the ensemble learning concept， the Stacking algorithm is used to train the residuals between the response values of the experimental data and the response values of the pre-training model， and then the residual model is established. Multi-source data fusion is carried out by combining the pre-trained model with the residual model to establish a high-precision digital twin model. Finally， the open-hole rectangular plate under axial tension is tested to validate the effectiveness of the DT-SSTM method.Results show that the DT-SSTM method can establish a high-precision digital twin model of the structural static test with higher global prediction accuracy， local prediction accuracy and data fusion efficiency compared with the similar data fusion methods， providing a novel solution for the real-time monitoring of structural stress fields.

Key words: digital twin, panel structure, data fusion, stress field monitoring, ensemble learning

CLC Number:

V214.4

Kuo TIAN, Zhiyong SUN, Zengcong LI. High-precision digital twin method for structural static test monitoring[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(7): 429134.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Fig.5

Table 2

Fig.6

Table 3

Fig.7

References 31

1	JIN S S， KIM S T， PARK Y H. Combining point and distributed strain sensor for complementary data-fusion： A multi-fidelity approach［J］. Mechanical Systems and Signal Processing， 2021， 157： 107725.
2	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.
3	LUO W， HU T， YE Y， et al. A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin［J］. Robotics and Computer-Integrated Manufacturing， 2020， 65： 101974.
4	LI L， LEI B， MAO C. Digital twin in smart manufacturing［J］. Journal of Industrial Information Integration， 2022， 26： 100289.
5	LI C， MAHADEVAN S， LING Y， et al. Dynamic Bayesian network for aircraft wing health monitoring digital twin［J］. AIAA Journal， 2017， 55（3）： 930-941.
6	LIM K Y H， ZHENG P， CHEN C H， et al. A digital twin-enhanced system for engineering product family design and optimization［J］. Journal of Manufacturing Systems， 2020， 57： 82-93.
7	TAO F， ZHANG H， LIU A， et al. Digital twin in industry： state-of-the-art［J］. IEEE Transactions on Industrial Informatics， 2019， 15（4）： 2405-2415.
8	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 X G， LAI X N， HE X W， et al. Key technologies of shape-performance integrated digital twin for major equipment ［J］. Journal of Mechanical Engineering， 2022， 58（10）： 298-325 （in Chinese）.
9	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.
10	XIA M， SHAO H， WILLIAMS D， et al. Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning［J］. Reliability Engineering & System Safety， 2021， 215： 107938.
11	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）： 023981.
	DONG L T， ZHOU X， ZHAO F B， et al. Key technologies for modeling and simulation of airframe digital twin［J］. Journal of Aeronautics， 2021， 42（3）： 023981 （in Chinese）.
12	李增聪，田阔，赵海心. 面向多级加筋壳的高效变保真度代理模型［J］. 航空学报， 2020， 41（7）： 623435.
	LI Z C， TIAN K， ZHAO H X. Efficient variable-fidelity models for hierarchical stiffened shells［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（7）： 623435 （in Chinese）.
13	TIAN K， LI Z C， HUANG L， et al. Enhanced variable-fidelity surrogate-based optimization framework by Gaussian process regression and fuzzy clustering［J］. Computer Methods in Applied Mechanics and Engineering， 2020， 366： 113045.
14	LI Z C， ZHANG S， LI H Q， et al. On-line transfer learning for multi-fidelity data fusion with ensemble of deep neural networks［J］. Advanced Engineering Informatics， 2022， 53： 101689.
15	GHOSH M， WU L， HAO Q， et al. A random forest with multi-fidelity Gaussian process leaves for modeling multi-fidelity data with heterogeneity［J］. Computers & Industrial Engineering， 2022， 174： 108746.
16	CHEN J， MENG C， GAO Y， et al. Multi-fidelity neural optimization machine for Digital Twins［J］. Structural and Multidisciplinary Optimization， 2022， 65（12）： 1-15.
17	LAI X， HE X， PANG Y， et al. A scalable digital twin framework based on a novel adaptive ensemble surrogate model［J］. Journal of Mechanical Design， 2023， 145（2）： 021701.
18	LI K， WANG S， LIU Y， et al. An integrated surrogate modeling method for fusing noisy and noise-free data［J］. Journal of Mechanical Design， 2022， 146（6）： 061701.
19	CAWLEY G C， TALBOT N L C. On over-fitting in model selection and subsequent selection bias in performance evaluation［J］. The Journal of Machine Learning Research， 2010， 11： 2079-2107.
20	NATEKIN A， KNOLL A. Gradient boosting machines， a tutorial［J］. Frontiers in Neurorobotics， 2013， 7： 21.
21	KRSTAJIC D， BUTUROVIC L J， LEAHY D E， et al. Cross-validation pitfalls when selecting and assessing regression and classification models［J］. Journal of Cheminformatics， 2014， 6（1）： 1-15.
22	SATAPATHY S K， BHOI A K， LOGANATHAN D， et al. Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal［J］. Biomedical Signal Processing and Control， 2021， 69： 102898.
23	GANAIE M A， HU M， MALIK A K， et al. Ensemble deep learning： A review［J］. Engineering Applications of Artificial Intelligence， 2022， 115： 105151.
24	TERRAULT N A， HASSANEIN T I. Management of the patient with SVR［J］. Journal of Hepatology， 2016， 65（1）： S120-S129.
25	LI H Q， LI Z C， CHENG Z Z， et al. A data-driven modelling and optimization framework for variable-thickness integrally stiffened shells［J］. Aerospace Science and Technology， 2022， 129： 107839.
26	HAO W Q， TAN L， YANG X G， et al. A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace［J］. International Journal of Fatigue， 2023， 170： 107536.
27	POH C Q X， UBEYNARAYANA C U， GOH Y M. Safety leading indicators for construction sites： A machine learning approach［J］. Automation in Construction， 2018， 93： 375-386.
28	TIAN K， LI Z C， ZHANG J X， et al. Transfer learning based variable-fidelity surrogate model for shell buckling prediction［J］. Composite Structures， 2021， 273： 114285.
29	SONG X G， LV L Y， SUN W， et al. A radial basis function-based multi-fidelity surrogate model： exploring correlation between high-fidelity and low-fidelity models［J］. Structural and Multidisciplinary Optimization， 2019， 60： 965-981.
30	ZHOU Q， SHAO X Y， JIANG P， et al. An active learning metamodeling approach by sequentially exploiting difference information from variable-fidelity models［J］. Advanced Engineering Informatics， 2016， 30（3）： 283-297.
31	TIAN K， WANG B， ZHANG K， et al. Tailoring the optimal load-carrying efficiency of hierarchical stiffened shells by competitive sampling［J］. Thin-Walled Structures， 2018， 133： 216-225.

方法	RRMSE	R²	RE_max	训练时间/s
有限元分析	0.340	0.813	18.18%
RBF （12 HF）	0.945	0.025	52.46%
GBDT （54 013 LF）	0.262	0.907	11.19%
Co-Kriging （12 HF+54 013 LF）				内存不足
Co-Kriging （12 HF+3 000 LF）	0.211	0.952	10.15%	1 737
ASF-RBF （12 HF+3 000 LF）	0.293	0.904	15.39%	2
ASF-RBF （12 HF+54 013 LF）	0.261	0.926	12.87%	2
TL-VFSM （12 HF+54 013 LF）	0.209	0.952	6.46%	5
DT-SSTM （12 HF+54 013 LF）	0.144	0.974	5.56%	1

传感器编号	Mises应力/MPa						数据源
传感器编号	试验	有限元	ASF-RBF	Co-Kriging	TL-VFSM	DT-SSTM	数据源
No.01	28.16	30.55	29.57	31.37	26.57	29.84	测试集
No.02	40.58	38.79	40.28	41.61	34.67	40.20	测试集
No.03	40.74	38.75	40.53	39.94	34.95	40.08	测试集
No.04	36.02	36.32	36.88	36.12	32.83	37.59	测试集
No.05	26.72	35.32	27.39	25.90	31.96	28.75	测试集
No.06	33.99	42.32	34.90	33.91	38.14	35.53	测试集
No.07	40.93	42.25	40.83	41.39	38.27	40.64	测试集
No.08	47.85	60.56	54.50	51.88	54.28	44.92	测试集
No.09	92.10	108.84	103.95	101.45	98.05	97.27	测试集
No.10	66.10	77.84	73.25	71.93	70.90	70.77	测试集
No.11	82.77	93.67	91.35	89.72	83.65	85.97	测试集
No.12	61.79	70.26	69.27	68.26	63.70	68.94	测试集
No.13	45.64	47.62	45.64	45.64	43.70	45.93	训练集
No.14	42.12	49.03	42.12	42.12	44.95	41.65	训练集
No.15	29.07	36.37	29.07	29.07	32.96	28.98	训练集
No.16	48.74	47.99	48.74	48.74	43.35	49.37	训练集
No.17	31.07	30.50	31.07	31.07	27.62	31.70	训练集
No.18	31.66	30.39	31.66	31.66	27.52	31.44	训练集
No.19	33.14	30.65	33.14	33.14	27.60	31.70	训练集
No.20	20.69	28.60	20.69	20.69	26.68	22.45	训练集
No.21	47.00	49.32	47.00	47.00	44.83	47.10	训练集
No.22	32.88	28.98	32.88	32.88	26.10	28.84	训练集
No.23	34.53	35.30	34.53	34.53	32.06	35.57	训练集
No.24	31.40	37.66	31.40	31.40	34.03	31.09	训练集

方法	RRMSE	R²	RE_max	训练时间/s
有限元分析	0.349	0.809	17.53%
Co-Kriging （12 HF+3 000 LF）	0.279	0.915	11.52%	2 191
ASF-RBF （12 HF+54 013 LF）	0.288	0.909	12.99%	2
TL-VFSM （12 HF+54 013 LF）	0.242	0.936	9.32%	5
DT-SSTM （12 HF+54 013 LF）	0.174	0.960	9.04%	1

[1]	Yingjie SHI, Binchao LIU, Songsong LU, Liang CHEN, Hai SHANG, Rui BAO. Neural network model for wing strain-load relationship based on fusion of real and virtual data [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 530921-530921.
[2]	Yiwei HUANG, Yibin GENG, Tianhe GAO, Xuanwei HU, Yuan WANG, Hongyan MA, Kuo TIAN. Digital twin driven high precision reconstruction method for full-field deformation of structure [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 530967-530967.
[3]	Dingqiang DAI, Xuan ZHOU, Leiting DONG, Xiasheng SUN. Research progress and prospects of digital engineering and digital twin in field of aeronautical fatigue and structural integrity [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531022-531022.
[4]	Shangyu LI, Hang FENG, Junquan CHEN, Bin CHEN, Dan MEI. A design architecture and conceptual modeling approach for digital twins [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531118-531118.
[5]	Junqi LEI, Yuehua CHENG, Bin JIANG, Cheng XU, Guili XU, Tianyu SUN. Digital-twin’s modelling and dynamic adjustment mechanism of rudder-loop-system under fault conditions [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531273-531273.
[6]	Liang CHEN, Lei HUANG, Yuxuan GU, Cong GUO, Kexin LIN, Yu GUAN, Jian SONG. Twinning technology of key part load based on flight parameters [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531292-531292.
[7]	Chengjie GUO, Dian XU, Jinbao LI, Chaoyu CHENG, Shuochang GUO, Rui LI. Stress characterization of high-temperature digital image correlation experiments based on a data fusion-knowledge transfer method [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531574-531574.
[8]	Yuxuan GU, Cong GUO, Lei HUANG, Yifei DONG, Hongda DONG, Zhilun DENG. Refined management of fleet life driven by digital twins [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531290-531290.
[9]	Yinxuan ZHANG, Qi ZHANG, Zhenyong XU, Linshu MENG. Predicting method of aircraft mechanical response based on residual neural networks [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531295-531295.
[10]	Ruoyao XIAO, Lianyu ZHENG, Jian ZHOU, Siru ZHAO, Jieru ZHANG, Yuwu CHEN. Online optimization method for positioning accuracy in cylindrical components aligning based on digital twins [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531978-531978.
[11]	Jialiang HU, Jiangpeng WU, Sixu HUO, Yidi GAO, Hua ZHENG. Modal parameter estimation based on reconstruction of digital twin sweep data in flutter flight test [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531602-531602.
[12]	Pengfei WANG, Lifang ZENG, Xueming SHAO, Jun LI. Multi-source data fusion modeling method for aerodynamic load of aircraft wing based on pre-training and fine-tuning [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 532297-532297.
[13]	Yifei WANG, Geyong CAO, Yang CAO, Xiaojun WANG. Uncertainty technologies in aircraft digital strength twins [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 532408-532408.
[14]	Liang CHEN, Fanxing MENG, Chengbo WANG, Yinxuan ZHANG, Linshu MENG. Development and application of digital twins technology in aircraft strength design [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 532252-532252.
[15]	Ran ZHUO, Chuliang YAN. A key component in digital twin of aircraft structures: Multi-dimensional flight parameter measurements [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 532375-532375.

High-precision digital twin method for structural static test monitoring

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 31

Related Articles 15

Recommended Articles

Metrics

Comments