Advertisement

Experimental Techniques

, Volume 44, Issue 1, pp 1–18 | Cite as

A Vision-Based Technique for in-Flight Measurement of Helicopter Blade Motion

  • E. Zappa
  • R. LiuEmail author
  • L. Trainelli
  • A. Rolando
  • P. Cordisco
  • M. Terraneo
  • M. Redaelli
Article
  • 75 Downloads

Abstract

The measurement of helicopter main rotor blade angles during flight is a key capability to implement advanced applications, such as strategies for the reduction of emitted noise and to develop innovative flight control laws. The approach proposed in this work for the real-time estimation of blade angles is based on a stereoscopic system mounted on the top of the main rotor and pointing to an optical target placed on the blade root. An advanced image-processing algorithm was developed to match the target features in the left and right camera images, which was required for the 3D reconstruction of the target based on a triangulation method. This algorithm was customized for the target used in this specific application, in order to implement a procedure that is both reliable in blob matching and characterized by a very low computation effort. This allowed the system to speed up the triangulation procedure aimed at obtaining the 3-D coordinates of the features, in view of real-time applications, even with very compact processing units that can be accommodated on the main rotor head. An inverse problem for the 3-D rotation of the target was solved using the Singular Value Decomposition technique, thus improving the robustness of the measurement. The stereoscopic system was developed in order to be integrated on board an AW139 helicopter main rotor hub, equipped with a synchronous lighting device and a pre-processing unit. The latter enabled the system to automatically extract the minimum set of information to be transferred, by means of a slipring, to the processing unit hosted in the fuselage. For the full assessment of the reliability and accuracy of the integrated system, harsh dynamic and accuracy trials were conducted on laboratory test benches. The harsh dynamic tests demonstrated that the system can work continuously in realistic conditions without any structural or data acquisition problems. The accuracy tests, based on a robot test rig simulating the motion of the blade, demonstrated the capability of the system and the accuracy of the measurement technique developed. The discrepancy between the reference blade angles and the estimated ones was found to be less than 0.360 for all the realistic blade angle combinations tested.

Keywords

Stereo camera Integrated measurement system on AW139 helicopter Advanced blob matching algorithm Accuracy test Harsh dynamic test 

Notes

Acknowledgements

The authors acknowledge the financial support provided by the MANOEUVRES project, framed within the Green RotorCraft (GRC) Integrated Technology Demonstrator of the European Union Clean Sky Joint Technology Initiative under Grant Agreement N. 620068.

References

  1. 1.
    Prouty R (2002) Helicopter performance, stability, and control. Krieger Publishing CompanyGoogle Scholar
  2. 2.
    You Y, Jung SN (2017) Optimum active twist input scenario for performance improvement and vibration reduction of a helicopter rotor. Aerosp Sci Technol 63:18–32CrossRefGoogle Scholar
  3. 3.
    Gennaretti M, Bernardini G, Serafini J, Romani G (2018) Rotorcraft comprehensive code assessment for blade–vortex interaction conditions. Aerosp Sci Technol 80:232–246CrossRefGoogle Scholar
  4. 4.
    Touron M, Dieulot J, Gomand J, Barre P (2018) A port-Hamiltonian framework for operator force assisting systems: application to the design of helicopter flight controls. Aerosp Sci Technol 72:493–501CrossRefGoogle Scholar
  5. 5.
    Wang X, Cai L (2015) Mathematical modeling and control of a tilt-rotor aircraft. Aerosp Sci Technol 47:473–492CrossRefGoogle Scholar
  6. 6.
    Jimenez GA, Barakos GN (2017) Numerical simulations on the ERICA tiltrotor. Aerosp Sci Technol 64:171–191CrossRefGoogle Scholar
  7. 7.
    Hanif A, Yousaf MH, Fazil A, Akhtar S (2014) Inflight helicopter blade track measurement using computer vision. 2014 IEEE Region, Symposium 10:56–61Google Scholar
  8. 8.
    Jiang M, Liang W, Zhang X (2012) Helicopter blades pyramid angle measurement based on panoramic vision technology. proceeding of the IEEE International Conference on Information and Automation:470–473Google Scholar
  9. 9.
    Lundstrom T, Baqersad J, Niezrecki C (2016) Monitoring the dynamics of a helicopter main rotor with high-speed stereophotogrammetry. Exp. Techniques 40:907–919CrossRefGoogle Scholar
  10. 10.
    Endo DMT, Montagnoli AN, Nicoletti R (2015) Measurement of shaft orbits with photographic images and sub-sampling technique. Exp Mech 55:471–481CrossRefGoogle Scholar
  11. 11.
    Wu R, Chen Y, Pan Y, Wang Q, Zhang D (2015) Determination of three-dimensional movement for rotary blades using digital image correlation. Opt Lasers Eng 65:38–45CrossRefGoogle Scholar
  12. 12.
    Rizo-Patron BS, Sirohi J (2017) Operational modal analysis of a helicopter rotor blade using digital image correlation. Exp Mech 57:367–375CrossRefGoogle Scholar
  13. 13.
    Wang YQ, Sutton MA, Ke XD, Schreier HW, Reu PL, Miller TJ (2011) On error assessment in stereo-based deformation measurements, part I: theoretical developments for quantitative estimates. Exp Mech 51:405–422CrossRefGoogle Scholar
  14. 14.
    Wu R, Kong C, Li K, Zhang D (2016) Real-time digital image correlation for dynamic strain measurement. Exp Mech 56:833–843CrossRefGoogle Scholar
  15. 15.
    Zappa E, Hasheminejad N (2017) Digital image correlation technique in dynamic applications on deformable targets. Exp Techniques 41:377–387CrossRefGoogle Scholar
  16. 16.
    Barrows DA, Burner AW, Abrego AI, Olson LE (2011) Blade displacement measurements of the full-scale UH-60A airloads rotor. In: 29th AIAA applied aerodynamics conferenceGoogle Scholar
  17. 17.
    Myers GC (1947) Flight measurements of helicopter blade motion with a comparison between theoretical and experimental results. In: National Advisory Committee for aeronautics technical note no. 1266Google Scholar
  18. 18.
    Trainelli L, Lovera M, Rolando A, Zappa E, Gennaretti M, Cordisco P et al (2015) Project MANOEUVRES – towards real-time noise monitoring and enhanced rotorcraft handling based on rotor state measurements. In: 41st European rotorcraft. ForumGoogle Scholar
  19. 19.
    Trainelli L, Gennaretti M, Zappa E et al (2016) Development and testing of innovative solutions for helicopter in-flight noise monitoring and enhanced control based on rotor state measurements. In: 42nd European rotorcraft. ForumGoogle Scholar
  20. 20.
    Trainelli L, Gennaretti M, Bernardini G et al (2016) Innovative helicopter in-flight noise monitoring enabled by rotor state measurements. Noise Mapping 3(190–215)Google Scholar
  21. 21.
    Gennaretti M, Bernardini G, Serafini J et al (2015) Acoustic prediction of helicopter unsteady manoeuvres. In: 41st European rotorcraft. ForumGoogle Scholar
  22. 22.
    Rolando A, Rossi F, Riboldi CED et al (2015) The pilot acoustic indicator: a novel cockpit instrument for the greener helicopter pilot. In: 41st European rotorcraft. ForumGoogle Scholar
  23. 23.
    Rolando A, Rossi F, Trainelli L, Leonello D, Maisano G, Redaelli M (2016) Demonstration and testing of the pilot acoustic indicator on a helicopter flight simulator. In: 42nd European rotorcraft. ForumGoogle Scholar
  24. 24.
    Panza S, Lovera M (2015) Rotor state feedback in the design of rotorcraft attitude control laws, advances in aerospace guidance, navigation and control. Springer, New YorkGoogle Scholar
  25. 25.
    Panza S, Lovera M, Bergamasco M, Viganò L (2015) Rotor state feedback in rotorcraft attitude control. In: 41st European rotorcraft. ForumGoogle Scholar
  26. 26.
    McKillip R (2002) A novel instrumentation system for measurement of helicopter rotor motions and loads data. American Helicopter Society 58th Annual ForumGoogle Scholar
  27. 27.
    Colombo, A., Locatell,i A.: Measuring blade angular motions: a kinematical approach. In: 30th European rotorcraft forum (2004)Google Scholar
  28. 28.
    Allred CJ, Jolly MR, Buckner GD (2015) Real-time estimation of helicopter blade kinematics using integrated linear displacement sensors. Aerosp Sci Technol 42:274–286CrossRefGoogle Scholar
  29. 29.
    Cigada A, Colombo A, Cordisco P et al (2016) Contactless rotor flapping sensor design, implementation and testing. American Helicopter Society 72nd Annual ForumGoogle Scholar
  30. 30.
    Zappa E, Trainelli L, Cordisco P et al (2016) A novel contactless sensor for helicopter blade motion in-flight measurements. In: 42nd European rotorcraft forumGoogle Scholar
  31. 31.
    Zappa E, Liu R, Trainelli L et al (2018) Laser and vision-based measurements of helicopter blade angles. Measurement 118:29–42CrossRefGoogle Scholar
  32. 32.
    Hartley RI, Sturm P (1997) Triangulation. Comput Vis Image Und 68:146–157CrossRefGoogle Scholar
  33. 33.
    Zhang Z (1999) Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of 7th IEEE international conference on computer vision, pp 666–673Google Scholar
  34. 34.
    Eggert DW, Lorusso A, Fisher RB (1997) Estimating 3-D rigid body transformations: a comparison of four major algorithms. Mach Vision Appl 9:272–290CrossRefGoogle Scholar
  35. 35.
    Wöhler C (2009) 3D computer vision: efficient methods and applications. Springer-Verlag, Berlin Heidelberg.  https://doi.org/10.1007/978-3-642-01732-2_2 CrossRefGoogle Scholar
  36. 36.
    Tippetts B, Lee DJ, Lillywhite K, Archibald J (January 2016) Review of stereo vision algorithms and their suitability for resource-limited systems. J Real-Time Image Proc 11(1):5–25CrossRefGoogle Scholar
  37. 37.
    Chaki N, Shaikh SH, Saeed K (2014) Exploring image binarization techniques. Springer, IndiaCrossRefGoogle Scholar
  38. 38.
    Zhang W, Li W, Yan J, Yu L, Pan C (2017) Adaptive threshold selection for background removal in fringe projection profilometry. Opt Lasers Eng 90:209–216CrossRefGoogle Scholar
  39. 39.
    Soille P (2013) Morphological image analysis: principles and applications. Springer Science & Business MediaGoogle Scholar
  40. 40.
    Wang ZZ (2016) Automatic segmentation and classification of the reflected laser dots during analytic measurement of mirror surfaces. Opt Lasers Eng 83:10–22CrossRefGoogle Scholar
  41. 41.
    Ghiringhelli GL, Terraneo M, Vigoni E (2013) Improvement of structures vibroacoustics by widespread embodiment of viscoelastic materials. Aerosp Sci Technol 28:227–241CrossRefGoogle Scholar
  42. 42.
    JCGM 100:2008 Evaluation of measurement data — Guide to the expression of uncertainty in measurement, https://www.bipm.org/en/publications/guides/gum.html
  43. 43.
    Schank TC, Schulte KJ (2015) A smart position sensor for articulated rotors. American Helicopter Society 71st Annual ForumGoogle Scholar
  44. 44.
    Redaelli M, Zappa E, Liu R et al (2017) In-flight demonstration of a novel contactless sensor for helicopter blade motion measurement. In: 28th annual Society of Flight Test Engineers European Chapter SymposiumGoogle Scholar

Copyright information

© The Society for Experimental Mechanics, Inc 2019

Authors and Affiliations

  • E. Zappa
    • 1
  • R. Liu
    • 1
    Email author
  • L. Trainelli
    • 2
  • A. Rolando
    • 2
  • P. Cordisco
    • 3
  • M. Terraneo
    • 3
  • M. Redaelli
    • 4
  1. 1.Department of Mechanical EngineeringPolitecnico di MilanoMilanItaly
  2. 2.Department of Aerospace Science and TechnologyPolitecnico di MilanoMilanItaly
  3. 3.VicoterCalolziocorteItaly
  4. 4.Leonardo HelicoptersCascina Costa di SamarateItaly

Personalised recommendations