Aeroelastic Vibration Measurement Based on Laser and Computer Vision Technique


The new aeronautical structures have become more flexible and light weight to reduce energy consumption, emissions, and noise, and to operate at high altitudes for long periods in the air, such as those required in the NASA Helios project. The increased structural flexibility of these aircraft has reignited concerns related to aeroelastic instabilities, such as flutter. Improving the techniques and methods used in aircraft certification flights is an important concern of the aeronautical community, because current standards and procedures do not provide recommendations and guidelines for aircraft with a high degree of flexibility. The techniques traditionally used in commercial airplanes cannot be used in this new aircraft concept, because they have a high degree of non-linearity in their flight dynamics. Current research indicates an increasing awareness about the importance of vision in the monitoring of UAV structural health. This work presents a new methodology to measure natural frequencies of aeronautical structures using a computer vision system. We also discusses new approaches to sense and acquire vibration data on aeroelastic certification flights test. These new approaches aim to reduce both the time required to identify the aeroelastic phenomenon and the size of the hardware that must be boarded on the aircraft, thus minimizing the risks and costs of the vibration tests. The advance of computer vision systems enables the use of cameras as a motion tracker sensor with millimeter precision and accuracy. Non-contact sensors are suitable for flutter analysis because they do not interfere with the dynamics of the aircraft. Therefore, this new methodology is able to process the obtained images and provide the user with the data about movements in a ready to use vector, at a reasonable cost. Using the data provided by this methodology the natural frequencies of the first bending modes were identified. This new methodology can be a user-friendly tool to support the Brazilian National Civil Aviation Agency - ANAC program called iBR 2020, which aims to certify small aircraft.

This is a preview of subscription content, access via your institution.

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
Fig. 14
Fig. 15
Fig. 16


  1. 1.

    TE Noll, SD Ishmael, B Henwood, ME Perez-Davis, GC Tiffany, J Madura, M Gaier, JM Brown, T Wierzbanowski (2007). Technical findings, lessons learned, and recommendations resulting from the helios prototype vehicle mishap. Tech. rep., National aeronautics and space admin langley research center hampton VA

  2. 2.

    Yael Maguire (2018). High altitude connectivity: The next chapter - Facebook Code (2018). he-next-chapter/

  3. 3.

    Tsushima N, Su W (2017) Flutter suppression for highly flexible wings using passive and active piezoelectric effects. Aerosp Sci Technol 65:78

    Article  Google Scholar 

  4. 4.

    Kayran A (2007) Flight flutter testing and aeroelastic stability of aircraft. Aircr Eng Aerosp Technol 79(2):150

    Article  Google Scholar 

  5. 5.

    AIRBOYD. Ressonância Aeroelástica - Efeito Flutter - YouTube.

  6. 6.

    S Sez (2010). Tacoma Narrows Bridge Collapse 1940 | Symon Sez.

  7. 7.

    RV Vaidyanathan, E Hemalatha (2010). Flight flutter testing and clearance of the baseline configuration of a developmental combat aircraft, Proceedings of ISMA 2010 - International Conference on Noise and Vibration Engineering, including USD 2010 pp. 3653–3666.

  8. 8.

    Saeed S, Salman S (2017) Flutter analysis of hybrid metal-composite low aspect ratio trapezoidal wings in supersonic flow. Chin J Aeronaut 30(1):196

    Article  Google Scholar 

  9. 9.

    J Sinske, G Jelicic, R Buchbach, J Schwochow, V Handojo (2017). Flight Testing Using Fast Online Aeroelastic Identification Techniques With Dlr Research Aircraft (June), 1

  10. 10.

    ANAC (2014). Programa de Fomento à Certificação de Projetos de Aviões de Pequeno Porte. s-e-programas/ibr2020/@@display-file/arquivo_norma/iBR2020.pdf

  11. 11.

    FLM dos Santos (2015). Strain-Base Experimental Modal Analysis: Advances in Theory and Practice. Ph.D. thesis, Instituto Tecnológico de Aeronáutica

  12. 12.

    R Brincker, C Ventura (2015). Introduction to operational modal analysis (John Wiley & Sons)

  13. 13.

    DE Raveh (2017). Assessment of Advanced Flutter Flight Test Techniques and Flutter Boundary Prediction (June), 1

  14. 14.

    PF Taylor, R Moreno, N Banavara, RK Narisetti, L Morgan (2017). Flutter Flight Testing at Gulfstream Aerospace Using Advanced Signal Processing Techniques, 58th AIAA/ASCE/AHS/ASC structures, structural dynamics, and materials conference (January), 1. DOI

  15. 15.

    ZY Pang, CES Cesnik, EM Atkins (2014). In - Flight Wing Deformation Measurement System for Small Unmanned Aerial Vehicles, 55th AIAA/ASMe/ASCE/AHS/SC structures, structural dynamics, and materials conference (January), 1. DOI

  16. 16.

    M Kurita, S Koike, K Nakakita, K Masui (2013). In-Flight Wing Deformation Measurement, 51st AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition (January), 1. DOI

  17. 17.

    AC Sanches (2019). Stereo vision-based technique to measure in-flight wing displacement of a very flexible aircraft. Dissertation of master of science

  18. 18.

    Chen JG, Wadhwa N, Cha YJ, Durand F, Freeman WT, Buyukozturk O (2015) Modal identification of simple structures with high-speed video using motion magnification. J Sound Vib 345:58.

    Article  Google Scholar 

  19. 19.

    Zhao X, Liu H, Yu Y, Zhu Q, Hu W, Li M, Ou J (2016) Displacement monitoring technique using a smartphone based on the laser projection-sensing method, sensors and actuators. A: Physical 246:35.

    CAS  Article  Google Scholar 

  20. 20.

    T.V. Jithin, S.K.C. P, G.K. N, Vibration analysis using machine vision system (2011), 1 (2017)

  21. 21.

    McCarthy DMJ, Chandler JH, Palmeri A (2013) Monitoring dynamic structural tests using image deblurring techniques. Key Eng Mater 569-570:932.

    Article  Google Scholar 

  22. 22.

    Feng D, Feng MQ (2016) Vision-based multipoint displacement measurement for structural health monitoring. Struct Control Health Monit 23(5):876.

    Article  Google Scholar 

  23. 23.

    Vicente M, Gonzalez D, Minguez J, Schumacher T (2018) A Novel Laser and Video-Based Displacement Transducer to Monitor Bridge Deflections. Sensors 18(4):970.

    Article  Google Scholar 

  24. 24.

    Castellini P, Martarelli M, Tomasini EP (2006) Laser Doppler Vibrometry: Development of advanced solutions answering to technology’s needs. Mech Syst Signal Process 20(6):1265.

    Article  Google Scholar 

  25. 25.

    Yoon H, Elanwar H, Choi H, Golparvar-Fard M, Spencer BF (2016) Target-free approach for vision-based structural system identification using consumer-grade cameras. Struct Control Health Monit 23(12):1405.

    Article  Google Scholar 

  26. 26.

    J Lau, J Debille, B Peeters, S Giclais, P Lubrina, M Böswald, Y Govers (2011). Advanced systems and services for ground vibration testing - Application for a research test on an Airbus A340–600 aircraft, proceedings “IFASD 2011” (January 2011).

  27. 27.

    D Feng, MQ Feng (2018). Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review, Eng Struct 156(may 2017), 105. DOI

  28. 28.

    Chen R, Li Z, Zhong K, Liu X, Chao YJ, Shi Y (2018) Low-speed-camera-array-based high-speed three-dimensional deformation measurement method: Principle, validation, and application. Opt Lasers Eng 107(January):21.

    Article  Google Scholar 

  29. 29.

    Sousa PJ, Barros F, Tavares PJ, Moreira PM (2018) Digital image correlation displacement measurement of a rotating RC helicopter blade. Eng Fail Anal 90(April):371.

    Article  Google Scholar 

  30. 30.

    D Zhang, J Guo, X Lei, C Zhu (2016). A high-speed vision-based sensor for dynamic vibration analysis using fast motion extraction algorithms, Sensors (Switzerland) 16(4). DOI

  31. 31.

    R Brincker, L Zhang, P Andersen (2000). Modal identification from ambient responses using frequency domain decomposition. &partnerID=tZOtx3y1

  32. 32.

    Brincker R et al (2001) Modal identification of output-only systems using frequency domain decomposition. Smart Mater Struct 10(3):441.

    Article  Google Scholar 

  33. 33.

    R Brincker, CE Ventura, P Andersen (2001). Damping estimation by frequency domain decomposition, 19th International Modal Analysis Conference (IMAC) pp. 698–703

  34. 34.

    JS Bendat, AG Piersol (2012). Random Data: Analysis and Measurement Procedures: Fourth Edition. DOI

  35. 35.

    Castillo Zuñiga DF (2019) Aeroelastic testing of flexible aircraft using acceleration and strain sensors. Thesis of doctor of science, Instituto Tecnológico de Aeronáutica

    Google Scholar 

  36. 36.

    Castillo Zuñiga DF, Souza A, Góes L (2020) Flight dynamics modeling of a flexible wing unmanned aerial vehicle. Mech Syst Signal Process 145:106900. 7

    Article  Google Scholar 

  37. 37.

    Machado RC (2019) Closed-loop subspace identification of an unmanned aerial system (UAS) with flexible wings. Thesis of doctor of science, Instituto Tecnológico de Aeronáutica

    Google Scholar 

  38. 38.

    D.F. Castillo Zuñiga, A. Giacobini Souza, L.C.S. Goes (2019). Development of an Aeroelastic In-Flight Testing System for a Flexible Wing Unmanned Aerial Vehicle using Acceleration and Strain Sensors (January). DOI

  39. 39.

    Cesnik CE, Palacios R, Reichenbach EY (2014) Reexamined Structural Design Procedures for Very Flexible Aircraft. J Aircr 51(5):1580.

    Article  Google Scholar 

Download references


The authors of this paper would like to acknowledge Prof. Dr. Luiz Carlos Sandoval Góes and Aeronautical System Laboratory members who contributed in some way for this work. The authors want to extend their special thanks to the Brüel & Kjaer for the technical support to perform the experiments described in this work. The authors are grateful for the funding of CAPES and FINEP.

Author information



Corresponding author

Correspondence to H.V. de Figueiredo.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

de Figueiredo, H., Castillo-Zúñiga, D., Costa, N. et al. Aeroelastic Vibration Measurement Based on Laser and Computer Vision Technique. Exp Tech 45, 95–107 (2021).

Download citation


  • Vibration Sensing Method
  • Image Processing
  • Vibration sensor
  • Aeroelastic testing
  • Operational Modal Analysis
  • Flexible aircraft
  • Aeroelastic certification
  • Modal testing
  • Computer Vision
  • Laser