Abstract
Vision-based vibration measurement has the great advantage that the displacement response of a whole surface, not a point or a line, can be measured at one time. However, the measurement using a camera has a problem in that the sampling rate is insufficient as compared with the conventional contact type sensor. The main objective of this paper is to propose a vibration measurement method in the hyper-Nyquist frequency range using a vision system. We also apply this method to implement a new vision-based modal analysis system. For the experimental modal analysis, we propose a new measurement method using features that the experimental designer can select and use the input signal of the excitation part. The input signal phase can be obtained in advance through the Hilbert transform process for the excitation input signal. Based on this phase signal, a specific trigger signal can be designed to make measurements at the desired instantaneous phase. All data acquisition process for the modal testing is operated with this trigger signal. We implemented the proposed method in hardware using an FPGA (field-programmable gate array) board. The feasibility of the suggested method was performed through the vibration measurement experiment using a cantilever beam. As a simple experiment on the cantilever beam to validate the proposed system, vibration measurement and modal analysis of the hyper-Nyquist frequency range were performed.
Similar content being viewed by others
References
Ewins DJ (2009) Modal testing: theory, practice and application. John Wiley & Sons
Kohut P, Kurowski P (2009) Application of modal analysis supported by 3D vision-based measurements. J Theor Appl Mech 47(4):855–870
Dossing O (1991) Prediction of transducer mass-loading effects and identification of dynamic mass. Proceedings of the 9th International Modal Analysis, p 306–312
Van der Auweraer H (2001) Structural dynamics modeling using modal analysis: applications, trends and challenges. Proceedings of the 2001 IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No. 01CH 37188). IEEE, vol. 3, p 1502–1509. https://doi.org/10.1109/IMTC.2001.929456
Xu Y, Brownjohn JM (2018) Review of machine-vision based methodologies for displacement measurement in civil structures. J Civ Struct Heal Monit 8(1):91–110
Chang CC, Ji YF (2007) Flexible videogrammetric technique for three-dimensional structural vibration measurement. J Eng Mech 133(6):656–664
Schreier H, Orteu JJ, Sutton MA (2009) Image correlation for shape, motion and deformation measurements. Springer USA
Acikgoz S, DeJong MJ, Soga K (2018) Sensing dynamic displacements in masonry rail bridges using 2D digital image correlation. Struct Control Health Monit 25(8):e2187
Schmidt T, Tyson J, Galanulis K (2003) Full-field dynamic displacement and strain measurement-specific examples using advanced 3D image correlation. Photogrammetry: Part II. Exp Tech 27(4):22–26
Synnergren P, Sjödahl M (1999) A stereoscopic digital speckle photography system for 3-D displacement field measurements. Opt Lasers Eng 31(6):425–443
Peeters B, Peeters K, Van der Auweraer H, Olbrechts T, Demeester F, Wens L (2004) Experimental modal analysis using camera displacement measurements: a feasibility study. Sixth international conference on vibration measurements by laser techniques: Advances and applications. Proceedings of SPIE, vol. 5503, SPIE, Bellingham, WA, p 298–309
Giergiel M, Kohut P (2011) Analysis of dynamics of vibratory machines applying vision based measurement. Mech Mech Eng 15:43–51
Lee SI, Chang SI (2015) Modal estimation of a cantilever using video images. In: INTER-NOISE and NOISE-CON Congress and Conference Proceedings. Institute of Noise Control Engineering, vol. 251, no. 1, p 694–701
Kim SC, Kim HK, Lee CG, Kim SB (2006) A vision system for identifying structural vibration in civil engineering constructions. In: 2006 SICE-ICASE International Joint Conference. IEEE, p 5813–5818. https://doi.org/10.1109/SICE.2006.315227
Xu Y, Brownjohn J, Kong D (2018) A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge. Struct Control Health Monit 25(5):e2155
Liu G, Li MZ, Mao Z, Yang QS (2022) Structural motion estimation via Hilbert transform enhanced phase-based video processing. Mech Syst Signal Process 166:108418
Yang Y, Dorn C, Mancini T, Talken Z, Kenyon G, Farrar C, Mascareñas D (2017) Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification. Mech Syst Signal Process 85:567–590
Valente NA, Sarrafi A, Mao Z, Niezrecki C (2022) Streamlined particle filtering of phase-based magnified videos for quantified operational deflection shapes. Mech Syst Signal Process 177:109233
Sarrafi A, Mao Z, Niezrecki C, Poozesh P (2018) Vibration-based damage detection in wind turbine blades using Phase-based Motion Estimation and motion magnification. J Sound Vib 421:300–318
Kim D, Park Y (2021) Vision-based modal testing of hyper-nyquist frequency range using time-phase transformation. In: Vibration engineering for a sustainable future. Springer, Cham, p 59–64. https://doi.org/10.1007/978-3-030-48153-7_8
Solav D, Moerman KM, Jaeger AM, Genovese K, Herr HM (2018) MultiDIC: An open-source toolbox for multi-view 3D digital image correlation. IEEE Access 6:30520–30535
Kahn-Jetter ZL, Chu TC (1990) Three-dimensional displacement measurements using digital image correlation and photogrammic analysis. Exp Mech 30(1):10–16
Blaber J, Adair B, Antoniou A (2015) Ncorr: open-source 2D digital image correlation matlab software. Exp Mech 55(6):1105–1122
MATLAB & Simulink (2021) What is camera calibration? https://www.mathworks.com/help/vision/ug/camera-calibration.html
Gloth G, Sinapius M (2004) Analysis of swept-sine runs during modal identification. Mech Syst Signal Process 18(6):1421–1441
Feldman M (2011) Hilbert transform in vibration analysis. Mech Syst Signal Process 25(3):735–802
Korpel A (1982) Gabor: frequency, time, and memory. Appl Opt 21(20):3624–3632
Bouguet JY, Perona P (1998) Camera calibration from points and lines in dual-space geometry. In: Proc. 5th European Conf. on Computer Vision. p 2–6
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Declarations
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kim, D., Park, Y. Vision-Based Modal Testing System for Hyper-Nyquist Frequency Range Using External Trigger Signal. Exp Tech 47, 1137–1147 (2023). https://doi.org/10.1007/s40799-022-00617-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40799-022-00617-x