Data-Driven Method for Real-Time Reconstruction of the Structural Displacement Field
Publication: Journal of Aerospace Engineering
Volume 37, Issue 3
Abstract
Accurate real-time displacement field reconstruction based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a data-driven displacement field reconstruction method called stacked convolutional autoencoder with denoising autoencoder and filter. Precise reconstruction of the structural displacement from a small number of local strains was made possible by the two primary components of the method: low-resolution displacement field reconstruction and result optimization. Given the significant imbalance between the limited strain information input and the structural displacement field output, a deep learning model with multiple deconvolution layers was built in the low-resolution displacement field reconstruction part using the layer-wise training property of a stacked autoencoder and the sparse mapping property of a convolutional neural network. The result optimization part utilized a denoising autoencoder and a linear density filter to effectively alleviate the checkerboard phenomenon and displacement field discontinuity caused by the deconvolution operation. The results of the case study indicate that the proposed method can accurately reconstruct the structural displacement field of both simple regular geometric structures and irregular geometric structures with complex boundaries without prior information. Additionally, the method exhibits excellent robustness to unavoidable measurement noise, providing a new implementation approach for real-time monitoring of spacecraft.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was financially supported by the National Key R&D Program of China (No. 2021YFA1003501), the Dalian City Supports Innovation and Entrepreneurship Projects for High-Level Talents (2021RD16), Liaoning Province’s Xing Liao Talents Program (XLYC2002108), and the Fundamental Research Funds for the Central Universities (DUT22QN251). This support is gratefully acknowledged.
References
Borate, P., G. Wang, and Y. Wang. 2020. “Data-driven structural health monitoring approach using guided lamb wave responses.” J. Aerosp. Eng. 33 (4): 04020033. https://doi.org/10.1061/(ASCE)AS.1943-5525.0001145.
Bruno, R., N. Toomarian, and M. Salama. 1994. “Shape estimation from incomplete measurements: A neural-net approach.” Smart Mater. Struct. 3 (2): 92. https://doi.org/10.1088/0964-1726/3/2/002.
Cerracchio, P., M. Gherlone, and A. Tessler. 2015. “Real-time displacement monitoring of a composite stiffened panel subjected to mechanical and thermal loads.” Meccanica 50 (10): 2487–2496. https://doi.org/10.1007/s11012-015-0146-8.
Chung, W., S. Kim, N.-S. Kim, and H. Lee. 2008. “Deflection estimation of a full scale prestressed concrete girder using long-gauge fiber optic sensors.” Constr. Build. Mater. 22 (3): 394–401. https://doi.org/10.1016/j.conbuildmat.2006.08.007.
Enzmann, M., C. Linz, and T. Theis. 1998. “Robust shape control of flexible structures using strain-measurement gauges and piezoelectric stack actuators.” In Proc., 4th European Conf. on Smart Materials and Structures, 123–130. Boca Raton, FL: CRC Press.
Foss, G., and E. Haugse. 1995. “Using modal test results to develop strain to displacement transformations.” In Proc., 13th Int. Modal Analysis Conf., 112. Bethel, CT: Society for Experimental Mechanics.
Ghasemzadeh, M., and A. Kefal. 2022. “Sensor placement optimization for shape sensing of plates and shells using genetic algorithm and inverse finite element method.” Sensors 22 (23): 9252. https://doi.org/10.3390/s22239252.
Gherlone, M., P. Cerracchio, and M. Mattone. 2018. “Shape sensing methods: Review and experimental comparison on a wing-shaped plate.” Prog. Aerosp. Sci. 99 (Jun): 14–26. https://doi.org/10.1016/j.paerosci.2018.04.001.
Gherlone, M., P. Cerracchio, M. Mattone, M. D. Sciuva, and A. Tessler. 2014. “An inverse finite element method for beam shape sensing: Theoretical framework and experimental validation.” Smart Mater. Struct. 23 (4): 045027. https://doi.org/10.1088/0964-1726/23/4/045027.
Glaser, R., V. Caccese, and M. Shahinpoor. 2012. “Shape monitoring of a beam structure from measured strain or curvature.” Exp. Mech. 52 (6): 591–606. https://doi.org/10.1007/s11340-011-9523-y.
Kang, L.-H., D.-K. Kim, and J.-H. Han. 2007. “Estimation of dynamic structural displacements using fiber Bragg grating strain sensors.” J. Sound Vib. 305 (3): 534–542. https://doi.org/10.1016/j.jsv.2007.04.037.
Kim, H.-I., L.-H. Kang, and J.-H. Han. 2011. “Shape estimation with distributed fiber Bragg grating sensors for rotating structures.” Smart Mater. Struct. 20 (3): 035011. https://doi.org/10.1088/0964-1726/20/3/035011.
Kingma, D. P., and J. Ba. 2017. “Adam: A method for stochastic optimization.” Preprint, submitted April 24, 2015. https://doi.org/10.48550/arXiv.1412.6980.
Li, C.-J., and A. G. Ulsoy. 1999. “High-precision measurement of tool-tip displacement using strain gauges in precision flexible line boring.” Mech. Syst. Signal Process. 13 (4): 531–546. https://doi.org/10.1006/mssp.1999.1223.
Liu, G., W. Luo, Y. Tong, and L. Fan. 2016. “Thermal deformation analysis method of in orbit whole cycle for spacecraft.” [In Chinese.] Spacecr. Eng. 25 (26): 40–47. https://doi.org/10.3969/j.issn.1673-8748.2016.06.007.
Luo, Z., J. Li, G. Hong, and H. Li. 2020. “Strain-based displacement field reconstruction method for thin rectangular plate through orthogonal deflection curves bridging.” Struct. Control Health Monit. 27 (1): e2457. https://doi.org/10.1002/stc.2457.
Mao, Z., and M. Todd. 2008. “Comparison of shape reconstruction strategies in a complex flexible structure.” In Proc., 15th Int. Symp. on: Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring. Bellingham, WA: International Society for Optics and Photonics. https://doi.org/10.1117/12.775931.
Meng, G., H. Hanhui, and X. Dingbang. 2012. “A displacement detection method for satellite phased-array antenna based on strain measurement.” Spacecr. Environ. Eng. 12 (6): 663–666. https://doi.org/10.3969/j.issn.1673-1379.2012.06.013.
Niu, S., Y. Zhao, and H. Bao. 2022. “Shape sensing of plate structures through coupling inverse finite element method and scaled boundary element analysis.” Measurement 190 (Feb): 110676. https://doi.org/10.1016/j.measurement.2021.110676.
Park, H. S., J. Y. Kim, J. G. Kim, S. W. Choi, and Y. Kim. 2013. “A new position measurement system using a motion-capture camera for wind tunnel tests.” Sensors 13 (9): 12329–12344. https://doi.org/10.3390/s130912329.
Park, S. W., H. S. Park, J. H. Kim, and H. Adeli. 2015. “3D displacement measurement model for health monitoring of structures using a motion capture system.” Measurement 59 (Jan): 352–362. https://doi.org/10.1016/j.measurement.2014.09.063.
Prechelt, L. 1998. “Early stopping—But when?” In Neural networks: Tricks of the trade, lecture notes in computer science, edited by G. B. Orr and K.-R. Müller, 55–69. Berlin: Springer.
Rapp, S., L.-H. Kang, J.-H. Han, U. C. Mueller, and H. Baier. 2009. “Displacement field estimation for a two-dimensional structure using fiber Bragg grating sensors.” Smart Mater. Struct. 18 (2): 025006. https://doi.org/10.1088/0964-1726/18/2/025006.
Rivera-Castillo, J., W. Flores-Fuentes, M. Rivas-López, O. Sergiyenko, F. F. Gonzalez-Navarro, J. C. Rodríguez-Quiñonez, D. Hernández-Balbuena, L. Lindner, and L. C. Básaca-Preciado. 2017. “Experimental image and range scanner datasets fusion in SHM for displacement detection.” Struct. Control Health Monit. 24 (10): e1967. https://doi.org/10.1002/stc.1967.
Roy, R., M. Gherlone, C. Surace, and A. Tessler. 2021. “Full-field strain reconstruction using uniaxial strain measurements: Application to damage detection.” Appl. Sci. 11 (4): 1681. https://doi.org/10.3390/app11041681.
Sun, Y., and J. H. L. Pang. 2006. “AFM image reconstruction for deformation measurements by digital image correlation.” Nanotechnology 17 (4): 933. https://doi.org/10.1088/0957-4484/17/4/016.
Torrey, L., and J. Shavlik. 2010. “Transfer learning.” In Handbook of research on machine learning applications and trends: Algorithms, methods, and techniques, 242–264. Hershey, PA: IGI Global.
Vurpillot, S., G. Krueger, D. Benouaich, D. Clément, and D. Inaudi. 1998. “Vertical deflection of a pre-stressed concrete bridge obtained using deformation sensors and inclinometer.” ACI Struct. J. 95 (Feb): 518–526. https://doi.org/10.14359/566.
Wang, L., M. Xu, D. Zhao, and G. Huang. 2023. “Rapid prediction of flexible deformations of a gust generator vane using data-driven approaches.” J. Aerosp. Eng. 36 (3): 04023009. https://doi.org/10.1061/JAEEEZ.ASENG-4659.
Wang, X., C. Si, Z. Wang, and Y. Li. 2021. “Displacement field reconstruction of structures under thermal and mechanical loading environment.” Aerosp. Sci. Technol. 117 (Feb): 106914. https://doi.org/10.1016/j.ast.2021.106914.
Wang, Z.-C., D. Geng, W.-X. Ren, and H.-T. Liu. 2014. “Strain modes based dynamic displacement estimation of beam structures with strain sensors.” Smart Mater. Struct. 23 (12): 125045. https://doi.org/10.1088/0964-1726/23/12/125045.
Wu, W., X. Ma, and J. Zhao. 2020. “Topological similarity measurement: A methodology for fast full-field deformation estimation.” Opt. Eng. 59 (12): 124106. https://doi.org/10.1117/1.OE.59.12.124106.
Yan, J., Y. Cheng, L. Zhang, Z. Li, T. Xu, F. Zhang, M. Jiang, and Q. Sui. 2023. “Displacement field reconstruction technique for plate-like structures based on model superposition method.” Meas. Control 56 (3–4): 654–667. https://doi.org/10.1177/00202940221095535.
Yang, H., S. Wang, H. Xia, J. Liu, A. Wang, and Y. Yang. 2022. “A prediction–correction method for fast and accurate initial displacement field estimation in digital image correlation.” Meas. Sci. Technol. 33 (10): 105201. https://doi.org/10.1088/1361-6501/ac7a06.
Yoneyama, S. 2016. “Basic principle of digital image correlation for in-plane displacement and strain measurement.” Adv. Compos. Mater. 25 (2): 105–123. https://doi.org/10.1080/09243046.2015.1129681.
Zhang, J., W. Wang, and Y. Lu. 2021. “Deformation field reconstruction method of elastic thin plate based on strain measurement.” [In Chinese.] Res. Explor. Lab. 40 (11): 14–19. https://doi.org/10.19927/j.cnki.syyt.2021.11.004.
Zhou, Q., L. Yan, L. Li, L. Feng, Y. Shi, P. Sun, L. Fang, and L. Wang. 2023. “On-orbit stability analysis of FXPT on XPNAV-1.” [In Chinese.] Acta Aeronaut. et Astronaut. Sin. 44 (03): 526610. https://doi.org/10.7527/S1000-6893.2021.26610.
Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 37 • Issue 3 • May 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jul 20, 2023
Accepted: Dec 19, 2023
Published online: Mar 9, 2024
Published in print: May 1, 2024
Discussion open until: Aug 9, 2024
ASCE Technical Topics:
- Aerospace engineering
- Aircraft and spacecraft
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Continuum mechanics
- Displacement (mechanics)
- Engineering fundamentals
- Engineering mechanics
- Environmental engineering
- Field tests
- Filters
- Filtration
- Material mechanics
- Materials engineering
- Neural networks
- Solid mechanics
- Strain
- Structural mechanics
- Tests (by type)
- Water treatment
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