基于数据合成的飞行器结构损伤状态快速识别方法

doi:10.12305/j.issn.1001-506X.2024.11.19

Abstract

Abstract:

In response to the complexity of the identification process and the low accuracy of identification in the current health monitoring process of aircraft structures, a rapid identification method for aircraft structural damage patterns based on data synthesis is proposed. A digital twin structure damage rapid identification model construction process is established to construct a digital model of aircraft structure. Based on the idea of data synthesis, a credibility evaluation method for sensor data is proposed, and an interpretable generative-discriminative model is established to solve the problem of low learning accuracy due to insufficient sample data. The method of fuzzy classification boundary is introduced, and the discriminative model is used to determine the fuzzy area to improve the stability of neural network identification. Finally, the proposed identification method is verified with a certain drone as an example. The results show that this method can efficiently build a structural damage pattern database and improve the generalization ability and stability of identification, with an identification accuracy rate of over 99% for damage patterns.

Key words: digital twin, structural health monitoring, damage detection, data synthesis, neural network

CLC Number:

Haoyuan WANG, Hua SU, Peng LI, Chunlin GONG. Rapid identification method for aircraft structural damage patterns based on data synthesis[J]. Systems Engineering and Electronics, 2024, 46(11): 3774-3783.

Figures/Tables 17

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Table 1

Table 2

Table 3

Table 4

References 33

1	张卫红, 唐长红. 航空航天装备的轻量化: 挑战与未来[J]. 航空学报, 2024, 45 (5): 9-15, 4.
	ZHANG W H , TANG C H . Lightweighting of aerospace and aeronautical equipment: challenges and perspectives[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45 (5): 9-15, 4.
2	王博, 郝鹏, 田阔, 等. 航空航天结构轻量化设计与实验方法研究进展[J]. 宇航学报, 2023, 44 (4): 596- 606.
	WANG B , HAO P , TIAN K , et al. Research progress on lightweight design and experimental methods for aerospace structures[J]. Journal of Astronautics, 2023, 44 (4): 596- 606.
3	VAN D W T , DENG Q , SANTOS B F . Robust long-term aircraft heavy maintenance check scheduling optimization under uncertainty[J]. Computers & Operations Research, 2022, 141, 105667.
4	YANG Z Y , YANG H J , TIAN T , et al. A review in guided-ultrasonic-wave-based structural health monitoring: from fundamental theory to machine learning techniques[J]. Ultrasonics, 2023, 133, 107014. doi: 10.1016/j.ultras.2023.107014
5	FIGUEIREDO E , BROWNJOHN J . Three decades of statistical pattern recognition paradigm for SHM of bridges[J]. Structural Health Monitoring, 2022, 21 (6): 3018- 3054. doi: 10.1177/14759217221075241
6	MONTAZERIAN H , RASHIDI A . Integrated sensors in advanced composites: a critical review[J]. Critical Reviews in Solid State and Materials Sciences, 2020, 45 (3): 187- 238. doi: 10.1080/10408436.2019.1588705
7	JU M , DOU Z , LI J W , et al. Piezoelectric materials and sensors for structural health monitoring: fundamental aspects, current status, and future perspectives[J]. Sensors, 2023, 23 (1): 543. doi: 10.3390/s23010543
8	SIMON J , KURIN T , MOLL J , et al. Embedded radar networks for damage detection in wind turbine blades: validation in a full-scale fatigue test[J]. Structural Health Monitoring, 2023, 22 (6): 4252- 4263. doi: 10.1177/14759217231152815
9	何存富, 郑明方, 吕炎, 等. 超声导波检测技术的发展、应用与挑战[J]. 仪器仪表学报, 2016, 37 (8): 1713- 1735. doi: 10.3969/j.issn.0254-3087.2016.08.004
	HE C F , ZHENG M F , LYU Y , et al. Development, applications and challenges in ultrasonic guided waves testing technology[J]. Chinese Journal of Scientific Instrument, 2016, 37 (8): 1713- 1735. doi: 10.3969/j.issn.0254-3087.2016.08.004
10	REN Y Q , QIU L , YUAN S F , et al. A diagnostic imaging approach for online characterization of multi-impact in aircraft composite structures based on a scanning spatial-wavenumber filter of guided wave[J]. Mechanical Systems and Signal Processing, 2017, 90, 44- 63. doi: 10.1016/j.ymssp.2016.12.005
11	REN B Y , LISSENDEN C J . PVDF multielement lamb wave sensor for structural health monitoring[J]. IEEE Trans.on Ultrasonics, Ferroelectrics, and Frequency Control, 2016, 63 (1): 178- 185. doi: 10.1109/TUFFC.2015.2496423
12	PAK C G . Wing shape sensing from measured strain[J]. American Institute of Aeronautics and Astronautics, 2016, 54 (3): 1068- 1077. doi: 10.2514/1.J053986
13	杜飞, 徐超, 鱼则行. 可重复使用运载器结构健康监测技术研究进展[J]. 宇航学报, 2019, 40 (10): 1177- 1186. doi: 10.3873/j.issn.1000-1328.2019.10.008
	DU F , XU C , YU Z X . Research progress on structural health monitoring technology for reusable launch vehicles[J]. Journal of Astronautics, 2019, 40 (10): 1177- 1186. doi: 10.3873/j.issn.1000-1328.2019.10.008
14	梁栋, 袁慎芳, 常琦, 等. 基于黑板协作的多区域冲击监测[J]. 系统工程与电子技术, 2011, 33 (3): 700- 706. doi: 10.3969/j.issn.1001-506X.2011.03.46
	LIANG D , YUAN S F , CHANG Q , et al. Multi-region impact monitoring based on blackboard coordination[J]. Systems Engineering and Electronics, 2011, 33 (3): 700- 706. doi: 10.3969/j.issn.1001-506X.2011.03.46
15	ERIC U . Fiber optic smart structures[J]. Proceedings of the IEEE, 1996, 84 (6): 884- 894. doi: 10.1109/5.503144
16	李鹏, 潘凯, 刘小川. 美国空军机体数字孪生计划的回顾与启示[J]. 航空科学技术, 2020, 31 (9): 1- 10.
	LI P , PAN K , LIU X C . Retrospect and enlightenment of the AFRL airframe digital twin program[J]. Aeronautical Science & Technology, 2020, 31 (9): 1- 10.
17	LI L N , SOHAIB A , ANDREW W , et al. Digital twin in aerospace industry: a gentle introduction[J]. IEEE Access, 2021, 10, 9543- 9562.
18	TAO F , XIAO B , QI Q L , et al. Digital twin modeling[J]. Journal of Manufacturing Systems, 2022, 64, 372- 389. doi: 10.1016/j.jmsy.2022.06.015
19	DANG H V , TATIPAMULA M , NGUYEN H X . Cloud-based digital twinning for structural health monitoring using deep learning[J]. IEEE Trans.on Industrial Informatics, 2021, 18 (6): 3820- 3830.
20	YE Y M , YANG Q A , YANG F , et al. Digital twin for the structural health management of reusable spacecraft: a case study[J]. Engineering Fracture Mechanics, 2020, 234, 107076. doi: 10.1016/j.engfracmech.2020.107076
21	KAPTEYN M G , KNEZEVIC D J , HUYNH D B P , et al. Data-driven physics-based digital twins via a library of component-based reduced-order models[J]. International Journal for Numerical Methods in Engineering, 2022, 123 (13): 2986- 3003. doi: 10.1002/nme.6423
22	王子一, 粟华, 龚春林, 等. 数字孪生机翼损伤模式快速识别与监测方法[J]. 航空动力学报, 2024, 39 (6): 112- 120.
	WANG Z Y , SU H , GONG C L , et al. Rapid identification and monitors of digital twin wings damage patterns[J]. Journal of Aerospace Power, 2024, 39 (6): 112- 120.
23	WANG J Y , LIU K X , ZHANG Y C , et al. Recent advances of few-shot learning methods and applications[J]. Science China Technological Sciences, 2023, 66 (4): 920- 944. doi: 10.1007/s11431-022-2133-1
24	刘建伟, 刘媛, 罗雄麟. 半监督学习方法[J]. 计算机学报, 2015, 38 (8): 1592- 1617.
	LIU J W , LIU Y , LUO X L . Semi-supervised learning methods[J]. Chinese Journal of Computers, 2015, 38 (8): 1592- 1617.
25	RAMIREZ-SANZ J M , MAESTRO- PRIETO J A , ARNAIZ-GONZALEZ A , et al. Semi-supervised learning for industrial fault detection and diagnosis: a systemic review[J]. ISA Transactions, 2023, 143, 255- 270. doi: 10.1016/j.isatra.2023.09.027
26	LIU B, WANG X D, DIXIT M, et al. Feature space transfer for data augmentation[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 9090-9098.
27	王钰清, 陆文凯, 刘金林, 等. 基于数据增广和CNN的地震随机噪声压制[J]. 地球物理学报, 2019, 62 (1): 421- 433.
	WANG Y Q , LU W K , LIU J L , et al. Random seismic noise attenuation based on data augmentation and CNN[J]. Chinese Journal of Geophysics, 2019, 62 (1): 421- 433.
28	GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proc. of the Advances in Neural Information Processing Systems, 2014: 2672-2680.
29	JIANG Y F, CHANG S Y, WANG Z Y. TransGAN: two pure transformers can make one strong GAN, and that can scale up[C]//Proc. of the Advances in Neural Information Processing Systems, 2021: 14745-14758.
30	王鹏, 杨妹, 祝建成, 等. 面向数字孪生的动态数据驱动建模与仿真方法[J]. 系统工程与电子技术, 2020, 42 (12): 2779- 2786. doi: 10.3969/j.issn.1001-506X.2020.12.14
	WANG P , YANG M , ZHU J C , et al. Dynamic data driven modeling and simulation method for digital twin[J]. Systems Engineering and Electronics, 2020, 42 (12): 2779- 2786. doi: 10.3969/j.issn.1001-506X.2020.12.14
31	方伟光, 聂兆伟, 刘宸宁, 等. 数字孪生驱动的武器装备智能保障技术研究[J]. 系统工程与电子技术, 2023, 45 (4): 1247- 1260. doi: 10.12305/j.issn.1001-506X.2023.04.35
	FANG W G , NIE Z W , LIU C N , et al. Research on digital twin driven intelligent weaponry support technology[J]. Systems Engineering and Electronics, 2023, 45 (4): 1247- 1260. doi: 10.12305/j.issn.1001-506X.2023.04.35
32	韩忠华. Kriging模型及代理优化算法研究进展[J]. 航空学报, 2016, 37 (11): 3197- 3225.
	HAN Z H . Kriging surrogate model and its application to design optimization: a review of recent progress[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37 (11): 3197- 3225.
33	ALTMAN N S . An introduction to kernel and nearest-neighbor nonparametric regression[J]. The American Statistician, 1992, 46 (3): 175- 185. doi: 10.1080/00031305.1992.10475879

区块	0、1等级边界	1、2等级边界	2、3等级边界	3、4等级边界
1	0.204 5	0.405 8	0.604 3	0.797 3
2	0.208 5	0.405 7	0.604 6	0.798 2
3	0.207 8	0.402 1	0.599 6	0.799 7
4	0.205 4	0.401 8	0.599 6	0.799 3
5	0.216 8	0.405 2	0.601 7	0.797 9
6	0.204 3	0.401 5	0.600 9	0.798 8

区块	0、1等级边界	1、2等级边界	2、3等级边界	3、4等级边界
1	0.019 2	0.034 4	0.044 6	0.049 5
2	0.034 2	0.049 3	0.054 2	0.055 5
3	0.054 4	0.057 1	0.057 3	0.057 7
4	0.054 1	0.057 2	0.057 7	0.057 6
5	0.045 9	0.055 2	0.056 2	0.057 0
6	0.040 1	0.048 3	0.051 5	0.053 8

区块	无边界模糊化识别准确率	使用边界模糊化识别准确率
1	97.2	99.2
2	94.3	99.6
3	94.0	99.4
4	93.2	99.2
5	96.7	99.8
6	97.1	100.0

区块	无边界模糊化识别准确率	使用边界模糊化识别准确率
1	99.7	100.0
2	99.0	100.0
3	98.1	100.0
4	96.3	100.0
5	96.9	100.0
6	99.3	100.0

[1]	Xiaobin LI, Dong XU, Xue YANG. Trajectory tracking control with predefined dynamic performance for underactuated autonomous underwater vehicle [J]. Systems Engineering and Electronics, 2024, 46(9): 3185-3197.
[2]	Tianqi ZHANG, Zongfang YANG, Han ZOU, Kunran MA. Blind identification algorithm for polarization code parameters based on encoding matrix estimation [J]. Systems Engineering and Electronics, 2024, 46(9): 3221-3230.
[3]	Lei WANG, Jin ZHANG, Qiuxuan YE. Spectrum sensing method based on cyclic spectrum and residual neural network in LDACS system [J]. Systems Engineering and Electronics, 2024, 46(9): 3231-3238.
[4]	Ruibin ZHANG, Mengtao ZHU, Yunjie LI. Radar transmitting signal generation method for modulation recognition network stealth [J]. Systems Engineering and Electronics, 2024, 46(7): 2256-2268.
[5]	Bing QI, Jianhua CHENG, Yanchi ZHAO, Zili WANG. Precise temperature drift error estimation method for capacitive MEMS accelerometers based on micro-deformation analysis [J]. Systems Engineering and Electronics, 2024, 46(7): 2437-2445.
[6]	Weiyi WU, Yunxian JIA, Xiangzheng JIANG, Xianming SHI, Jie LIU, Bin LIU, Enzhi DONG, Xi ZHU. Method for determining for carrying material varieties of stage task [J]. Systems Engineering and Electronics, 2024, 46(6): 2054-2064.
[7]	Hao QING, Zhigeng FANG, Yuhong WANG, Xirui QIU. Combination prediction of civil aircraft demand based on grey-neural network [J]. Systems Engineering and Electronics, 2024, 46(5): 1665-1672.
[8]	Zongfang YANG, Tianqi ZHANG, Kunran MA, Han ZOU. Blind identification of channel coding types based on deep neural networks [J]. Systems Engineering and Electronics, 2024, 46(5): 1820-1829.
[9]	Tong HE, Qing LU, Jun ZHOU, Zongyi GUO. Line-of-sight angle constraint guidance with neural network interference observer [J]. Systems Engineering and Electronics, 2024, 46(4): 1372-1382.
[10]	Hongjin ZHOU, Hui SONG, Wenliang FAN, Su WANG, Dongliang GU. Ship inertial navigation system position correction method based on Bayesian neural network [J]. Systems Engineering and Electronics, 2024, 46(4): 1393-1400.
[11]	Xianpeng MENG, Limin LIU, Jian DONG, Li WANG, Wenhua HU. Radar frequency agility behavior recognition based on bi-cell recurrent neural network [J]. Systems Engineering and Electronics, 2024, 46(3): 898-905.
[12]	Lei YU, Qiuyue DENG, Liying ZHENG, Haoyu WU. Second-order progressive feature fusion network for image super-resolution reconstruction [J]. Systems Engineering and Electronics, 2024, 46(2): 391-400.
[13]	Kun WANG, Xinran DUAN, Zheng CHEN, Jun LI. Nonlinear optimal guidance method with constraints on overload and impact time [J]. Systems Engineering and Electronics, 2024, 46(2): 649-657.
[14]	Bowen XIAO, Zeyuan MA, Tianyu LU, Qunli XIA. On-line identification method for models of platform seeker disturbance rejection rate [J]. Systems Engineering and Electronics, 2024, 46(11): 3595-3604.
[15]	Gang LEI, Canhui LAI, Yunshu LI, Wei LUO. A fast calculation method for electronic interference strategy under mobile launch conditions [J]. Systems Engineering and Electronics, 2024, 46(11): 3648-3657.

Rapid identification method for aircraft structural damage patterns based on data synthesis

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 17

References 33

Related Articles 15

Recommended Articles

Metrics

Comments