Non-contact full-field vibration measurement using 3D digital image correlation: Experimental validation against accelerometers in multi-axis dynamic testing

Suleiman  Ibrahim Mohammad; Asokan  Vasudevan; Seif Al  Bustanji

doi:10.59400/sv4129

Non-contact full-field vibration measurement using 3D digital image correlation: Experimental validation against accelerometers in multi-axis dynamic testing

Suleiman Ibrahim Mohammad
Research Fellow, INTI International University, Nilai 71800, Malaysia
Asokan Vasudevan
Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia
Seif Al Bustanji
Faculty of Technical Education, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan

Article ID: 4129

Keywords: 3D digital image correlation; full-field vibration measurement; multi-axis dynamic testing; frequency response function; modal analysis; measurement uncertainty; structural dynamics validation

Abstract

Accurate measurement of such vibrations is a prerequisite for any research work in the field of structural dynamics, modal analysis, and evaluation of machinery reliability. Although accelerometers are traditionally used for experimental vibration analysis, their use is also associated with certain limitations such as mass loading effects and under-sampling issues. Non-contact full-field vibration measurement techniques such as three-dimensional digital image correlation (3D-DIC) have also emerged as potential alternatives for vibration measurement. However, their comprehensive evaluation for full-field vibration measurement under broadband random excitations is still limited. This paper presents an experimental evaluation of the full-field vibration measurement technique using 3D-DIC for vibration measurement. The performance evaluation of the technique is also carried out using a conventional vibration measurement technique such as accelerometers. A ribbed aluminium cantilever plate is subjected to controlled broadband random excitations, and the vibration response is measured using a stereo high-speed full-field vibration measurement technique such as 3D-DIC and a triaxial accelerometer. The comparative analysis showed considerable concurrence between the two systems in the assessment of the displacement amplitude tracking, resonance identification, and modal shape characterization. Statistical evaluation showed the absence of any statistically significant difference in the mean displacement measurement within the established industrial tolerance limit. The optical system provided continuous spatial resolution and multi-axis displacement mapping without the application of any structural mass loading. This shows the equivalence and the industrial applicability of the 3D-DIC method for broadband multi-axis dynamic testing, thus making it suitable for the analysis of the vibration behaviour of the structure.

Published

2026-06-05

How to Cite

Ibrahim Mohammad, S., Vasudevan, A., & Bustanji, S. (2026). Non-contact full-field vibration measurement using 3D digital image correlation: Experimental validation against accelerometers in multi-axis dynamic testing. Sound & Vibration, 60(3). https://doi.org/10.59400/sv4129

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Vol. 60 No. 3 (2026): In Progress

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Vogl GW, Harper KK, Payne B. Modelling and experimental analysis of piezoelectric shakers for high-frequency calibration of accelerometers. AIP Conference Proceedings. 2010; 1253: 383–394. doi: 10.1063/1.3455480

[2]Bartels J, Xu R, Kang C, et al. Experimental investigation on the transfer behaviour and environmental influences of low-noise integrated electronic piezoelectric acceleration sensors. Metrology. 2024; 4(1): 46–65. doi: 10.3390/metrology4010004

[3]Sarrafi A, Mao Z, Niezrecki C, et al. Vibration-based damage detection in wind turbine blades using phase-based motion estimation and motion magnification. Journal of Sound and Vibration. 2018; 421: 300–319. doi: 10.1016/j.jsv.2018.01.050

[4]Daly S. Digital Image Correlation in Experimental Mechanics for Aerospace Materials and Structures. In: Encyclopedia of Aerospace Engineering. Wiley; 2010. doi: 10.1002/9780470686652.eae542

[5]Murray CA, Hoult NA, Take WA. Dynamic measurements using digital image correlation. International Journal of Physical Modelling in Geotechnics. 2017; 17(1): 41–52. doi: 10.1680/jphmg.15.00055

[6]Seo S, Ko YH, Chung M. Evaluation of field applicability of high-speed 3D digital image correlation for shock vibration measurement in underground mining. Remote Sensing. 2022; 14(13): 3133. doi: 10.3390/rs14133133

[7]Ferrero R, Gandino F, Hemmatpour M, et al. Exploiting accelerometers to estimate displacement. In: Proceedings of the 5th Mediterranean Conference on Embedded Computing (MECO); 12–16 June 2016; Bar, Montenegro. pp. 206–212. doi: 10.1109/meco.2016.7525741

[8]Wu P, Li W, Zhao X. Displacement sensing based on microscopic vision with high resolution and large measuring range. Computer-Aided Civil and Infrastructure Engineering. 2024; 39(18): 2840–2858. doi: 10.1111/mice.13227

[9]Vinel A, Seghir R, Berthe J, et al. Metrological assessment of multi-sensor camera technology for spatially-resolved ultra-high-speed imaging of transient high strain-rate deformation processes. Strain. 2021; 57(4): e12381. doi: 10.1111/str.12381

[10]Patuelli C, Cestino E, Frulla G. A beam finite element for static and dynamic analysis of composite and stiffened structures with bending-torsion coupling. Aerospace. 2023; 10(2): 142. doi: 10.3390/aerospace10020142

[11]Saunders H. Book Reviews: Modal Testing: Theory and Practice. Journal of Vibration, Acoustics, Stress, and Reliability in Design. 1986; 108(1): 109–110. doi: 10.1115/1.3269294

[12]Pan B, Kemao Q, Xie H, et al. Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Measurement Science and Technology. 2009; 20(6): 062001. doi: 10.1088/0957-0233/20/6/062001

[13]Hagara M, Huňady R. The influence of sampling frequency on the results of motion analysis performed by high-speed digital image correlation. Applied Mechanics and Materials. 2015; 816: 397–403. doi: 10.4028/www.scientific.net/amm.816.397

[14]Trebuňa F, Huňady R, Bobovský Z, et al. An application of high-speed digital image correlation in determination of modal parameters. Acta Mechanica Slovaca. 2011; 15(4): 6–12. doi: 10.21496/ams.2011.034

[15]Liu W, Zhu J, Li W, et al. High-precision stereo calibration for 3D measurement via speckle-enhanced circular control point matching. Applied Optics. 2025; 64(13): 3561–3570. doi: 10.1364/ao.560401

[16]Neumayer M, Bretterklieber T. Noise and uncertainty analysis for time and frequency domain vibration measurements using acceleration sensors. In: Proceedings of the IEEE International Instrumentation and Measurement Technology Conference (I2MTC); 22–25 May 2023; Kuala Lumpur, Malaysia. doi: 10.1109/i2mtc53148.2023.10176079

[17]Gioffrè M, Gusella V, Marsili R, et al. Comparison between accelerometer and laser vibrometer to measure traffic-excited vibrations on bridges. In: Proceedings of the 4th International Conference on Vibration Measurement by Laser Techniques; 20–23 June 2000; Ancona, Italy. doi: 10.1117/12.386764

[18]Ben-Aryeh Y. Super-resolution measurements related to uncertainty relations in optical and biological fluorescence systems. Journal of Quantitative Spectroscopy and Radiative Transfer. 2013; 131: 43–54. doi: 10.1016/j.jqsrt.2013.04.008

[19]Chen Y, Avitabile P, Dodson J, et al. Data consistency assessment function (DCAF). Mechanical Systems and Signal Processing. 2020; 141: 106688. doi: 10.1016/j.ymssp.2020.106688

[20]Richardson S, Tyler J, McHargue P, et al. A new measure of shape difference. In: Wicks, A. (editor). Structural Health Monitoring. Springer; 2014; 5, pp. 71–80. doi: 10.1007/978-3-319-04570-2_8

[21]Mottershead JE, Foster CD. On the treatment of ill-conditioning in spatial parameter estimation from measured vibration data. Mechanical Systems and Signal Processing. 1991; 5(2): 139–154. doi: 10.1016/0888-3270(91)90020-6

[22]Periasamy C, Tippur HV. A full-field reflection-mode digital gradient sensing method for measuring orthogonal slopes and curvatures of thin structures. Measurement Science and Technology. 2013; 24(2): 025202. doi: 10.1088/0957-0233/24/2/025202

[23]Patuelli C, Polla A, Cestino E, et al. Experimental and numerical dynamic behaviour of bending-torsion coupled box-beam. Journal of Vibration Engineering & Technologies. 2022; 11(7): 3451–3463. doi: 10.1007/s42417-022-00759-7

[24]Sutton MA. Digital image correlation for shape and deformation measurements. In: Sharpe W (editor). Springer Handbook of Experimental Solid Mechanics. Springer; 2008. pp. 565–600. doi: 10.1007/978-0-387-30877-7_20

[25]Siebert T, Schubach HR, Splitthof K, et al. Recent developments and applications for optical full field strain measurement using ESPI and DIC. In: Proceedings of the Fourth International Seminar on Modern Cutting and Measuring Engineering; 10–12 December 2010; Beijing, China. doi: 10.1117/12.891864

[26]de Souza Mello FM, Pereira JLJ, Gomes GF. Multi-objective sensor placement optimization in SHM systems with Kriging-based mode shape interpolation. Journal of Sound and Vibration. 2023; 568: 118050. doi: 10.1016/j.jsv.2023.118050

[27]Niezrecki C, Avitabile P, Warren C, et al. A review of digital image correlation applied to structural dynamics. AIP Conference Proceedings. 2010; 1253: 219–232. doi: 10.1063/1.3455461

[28]Reu PL, Miller TJ. The application of high-speed digital image correlation. The Journal of Strain Analysis for Engineering Design. 2008; 43(8): 673–688. doi: 10.1243/03093247jsa414

[29]Pan B. Accurate, Fast, and Robust Digital Image Correlation. SPIE Newsroom; 2013. doi: 10.1117/2.1201307.005032

[30]Grover DJ. Low-frequency drift reduction in the integration of repetitive signals. Proceedings of the Institution of Electrical Engineers. 1966; 113(5): 895–902. doi: 10.1049/piee.1966.0149

[31]Castellini P, Paone N. Development of the tracking laser vibrometer: performance and uncertainty analysis. Review of Scientific Instruments. 2000; 71(12): 4639–4647. doi: 10.1063/1.1319862

[32]Dorn C, Yang Y. Automated modal identification by quantification of high-spatial-resolution response measurements. Mechanical Systems and Signal Processing. 2022; 186: 109816. doi: 10.1016/j.ymssp.2022.109816

[33]Deng T, Wang Y, Huang J, et al. Automatic high-resolution operational modal identification of thin-walled structures supported by high-frequency optical dynamic measurements. Materials. 2024; 17(20): 4999. doi: 10.3390/ma17204999

[34]Li R, Zhang Y, Wang M, et al. Sparse strain sensing-based bending-torsion coupled deformation measurement in discontinuous stiffness open-hole CFRP panels using iFEM. Measurement Science and Technology. 2026; 37(4): 045207. doi: 10.1088/1361-6501/ae3b62

[35]Yang S, Meng D, Alfouneh M, et al. A robust-weighted hybrid nonlinear regression for reliability based topology optimization with multi-source uncertainties. Computer Methods in Applied Mechanics and Engineering. 2025; 447: 118360. doi: 10.1016/j.cma.2025.118360

[36]Meng D, Yang S, Jesus AMPD, et al. A novel hybrid adaptive Kriging and water cycle algorithm for reliability-based design and optimization strategy: application in offshore wind turbine monopile. Computer Methods in Applied Mechanics and Engineering. 2023; 412: 116083. doi: 10.1016/j.cma.2023.116083

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