Skip to main content
Log in

Aircraft wing vibration parameters measurement system using MEMS IMUs and closed-loop optimal correction

  • Original Paper
  • Published:
Aerospace Systems Aims and scope Submit manuscript

Abstract

Nowadays, there is still a need for the development of a high-precision measurement system for aircraft structural elements vibrations, this is due to the fact that many aviation problems are associated with analyzing vibration characteristics of aircraft mechanical structures during its main modes of operation, including flight, particularly the airplane wing health monitoring. This paper presents preliminary results of research carried out toward building a promising system designed to measure vibration parameters of airplane wing. Comparing it with the existing analogues systems, the proposed system features the use of approaches that are traditional for solving orientation and navigation problems for vibration measurements. The article discusses the principles of building such system based on micromechanical inertial measurements units, displacement sensors, on-board aircraft navigation system and sensor data fusion technology. The paper provides a brief overview of the existing solutions in this field of research, and substantiates the relevance and feasibility of the proposed technical solution. The paper presents the basic hardware components of the proposed system, presents the fundamentals of its operation based on the data collected from many sources, including displacement sensors, inertial units and the optimal Kalman estimation and correction algorithm. Besides, the paper presents the mathematical errors models of the system main components. The main algorithms of the system are shown, including algorithms for inertial measurements, estimation and correction in a closed-loop scheme for including optimal Kalman filter, and indirect calculations of vibration parameters. Finally, the initial simulation results of system operation are shown, demonstrating its operability and expected accuracy characteristics, which confirms the system effectiveness and the prospects of the chosen direction of research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Krysinski T, Malburet F (2010) Mechanical vibrations: active and passive control, 103rd edn. Wiley

    MATH  Google Scholar 

  2. Burns VA, Lysenko EA, Marinin DA, Dolgopolov AV, Zhukov EP (2015) Identification.of aircraft defects by vibration parameters during operation. Acad Sci Higher Sch Rus Fed Rep 2:24–42

    Google Scholar 

  3. Komarov VA, Lapteva MYu (2011) Predicting wing deformations [Russian resource]. All-RussianScientific-Technical Journal "Polyot" ("Flight"), (3), pp7–12

  4. Parputs AA, Pankeev ES, Musonov VM (2015) Vibration testing of aircraft structures [Russian source]. Actual problems of aviation and astronautics, vol 1, no 11, pp 714–715

  5. Feigenbaum YuM, Butushin SV, Bozhevalov DG, Sokolov YuS (2015) Composite materials and the history of their implementation in aircraft structures. Sci Bull GosNII GA 7:24–37

    Google Scholar 

  6. Katunin A, Dragan K, Dziendzikowski M (2015) Damage identification in aircraft composite structures: a case study using various non-destructive testing techniques. Compos Struct 127:1–9

    Article  Google Scholar 

  7. Pang ZY, Cesnik CE, Atkins EM (2014) In-Flight Wing Deformation Measurement System for Small Unmanned Aerial Vehicles. In 55th AIAA/ASMe/ASCE/AHS/SC Structures, Structural Dynamics, and Materials Conference p. 0330

  8. Alvarez-Montoya J, Carvajal-Castrillón A, Sierra-Pérez J (2020) In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition. Mech Syst Signal Process 136:106526

    Article  Google Scholar 

  9. Varanis M, Silva A, Mereles A, Pederiva R (2018) MEMS accelerometers for mechanical vibrations analysis: a comprehensive review with applications. J Braz Soc Mech Sci Eng 40(11):1–18

    Article  Google Scholar 

  10. Govers Y, Sinske J, Petzsche T (2020) Latest design trends in modal accelerometers for aircraft ground vibration testing. Sensors and instrumentation, aircraft/aerospace, energy harvesting & dynamic environments testing, vol 7. Springer, Cham, pp 97–106

    Google Scholar 

  11. Aggarwal P (2010) MEMS-based integrated navigation. Artech House

    Google Scholar 

  12. Hemerly EM (2017) MEMS IMU stochastic error modelling. Syst Sci Contr Eng 5(1):1–8

    Article  Google Scholar 

  13. Syed ZF, Aggarwal P, Goodall C, Niu X, El-Sheimy N (2007) A new multi-position calibration method for MEMS inertial navigation systems. Meas Sci Technol 18(7):1897

    Article  Google Scholar 

  14. Kaswekar P, Wagner JF (2015) September. Sensor fusion based vibration estimation using inertial sensors for a complex lightweight structure. In 2015 DGON Inertial Sensors and Systems Symposium (ISS) (pp. 1–20). IEEE

  15. Chui CK, Chen G (2017) Kalman filtering. Springer International Publishing, Berlin, pp 19–26

    Book  Google Scholar 

  16. Afonin AA, Sulakov AS, Maamo MS (2021) Application of optimal kalman filter to improve the accuracy of aircraft wing vibration parameters measurement system. J Phys Conf Ser 2096(1):012182

    Article  Google Scholar 

  17. Rivkin SS, Ivanovsky RI, Kostrov AV (1976) Statistical optimization of navigation systems [Russian source]. Shipbuilding in Leningrad, p 280

    Google Scholar 

  18. Afonin AA, Sulakov AS (2011) About the correction of the parameters of orientation, navigation and gravimetry in a closed circuit for switching on the Kalman filter of the navigation complex. Aerosp Instrument-Making 8:17–24

    Google Scholar 

  19. Tyvin AV, Afonin AA, Chernomorsky AI (2005) About one concept of vector gravimetric.measurements. J Aerosp Instrum 3:24–29

    Google Scholar 

  20. Alyoshin BS, Afonin AA, Veremeenko KK et al (2006) Orientation and navigation of movable objects: Modern Information Technologies [Russian source], Moscow Fizmatlit, p 421

    Google Scholar 

  21. Andreev VD (1966) Inertial Navigation Theory: Autonomous Systems [Russian source]. Publishing house "Science", Head. ed. physics and mathematics literature, p 579

Download references

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. S. Maamo.

Ethics declarations

Conflict of interest

The authors have not disclosed any competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maamo, M.S., Afonin, A.A. & Sulakov, A.S. Aircraft wing vibration parameters measurement system using MEMS IMUs and closed-loop optimal correction. AS 5, 473–480 (2022). https://doi.org/10.1007/s42401-022-00145-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42401-022-00145-x

Keywords

Navigation