Volume 21, Issue 1 (January 2021)                   Modares Mechanical Engineering 2021, 21(1): 39-53 | Back to browse issues page

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Afsanehbekrpoushideh@gmail.com. Modal Identification of Structures via Processing of Recorded Videos and Output-only Algorithms. Modares Mechanical Engineering 2021; 21 (1) :39-53
URL: http://mme.modares.ac.ir/article-15-49390-en.html
Abstract:   (1911 Views)
One of the measurement systems for the identification of modal parameters of the structure is digital video cameras. Modal analysis based on video measurements, despite the many advantages, is associated with some challenges due to its dependence on high contrast markers. In the present study, a new algorithm is presented to use only the measured full-field responses, without additional preparation of the structural surface. This algorithm is phase-based and is implemented using the blind source separation method and motion magnification technique. It uses a multi-scale pyramid analysis technique to extract the full-field spatiotemporal pixel phases. To validate this algorithm, the free and random vibration videos of two cantilever and simple beams with known modal parameters were reconstructed in MATLAB. The average difference between the values identified and the theoretical values for the frequencies of the first to fourth modes is less than 2% and less than 0/1 for damping. The results obtained in this section also confirm the ability of the algorithm to identification closely-spaced modes of the structure. Also, to evaluate the performance of the algorithm in laboratory conditions, a free and random vibration video of an aluminum cantilever beam, prepared in the laboratory using a high-speed camera, is examined. Comparing the results with theoretical values or case study reports shows that using the techniques introduced in this article is a suitable and promising solution to identify the modal parameters of the structure.
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Article Type: Original Research | Subject: Impact Mechanics
Received: 2021/01/21 | Accepted: 2021/01/19 | Published: 2021/01/19

References
1. Doebling SW, Farrar CR, Prime MB. A summary review of vibration-based damage identification methods. The Shock and Vibration Digest. 1998;30(2):91–105.
2. Ewins DJ. Modal testing: theory, practice and application. John Wiley & Sons; 2009 Jul 20.
3. Yang Y, Dorn C, Mancini T, Talken Z, Nagarajaiah S, Kenyon G, Farrar C, Mascareñas D. Blind identification of full-field vibration modes of output-only structures from uniformly-sampled, possibly temporally-aliased (sub-Nyquist), video measurements. Journal of Sound and Vibration. 2017;390:232-56.
4. Fan W, Qiao P. Vibration-based damage identification methods: a review and comparative study. Structural health monitoring. 2011;10(1):83-111.
5. Khatybi MM, Ashory MR. Estimation of natural frequencies using mass-cancellation method in operational modal testing. Modares Mechanical Engineering. 2014;14(8):183-192.
6. Stanbridge A, Ewins D. Modal Testing Using A scanning laser doppler vibrometer. Mechanical Systems and Signal Processing. 1999;13(2):255–70.
7. Castellini P, Martarelli M, Tomasini E. Laser Doppler vibrometry: development of advanced solutions answering to technology's needs. Mechanical Systems and Signal Processing. 2006;20(6):1265–85.
8. Di Maio D, Ewins DJ. Continuous Scan, a method for performing modal testing using meaningful measurement parameters; Part I. Mechanical Systems and Signal Processing. 2011;25(8):3027-42.
9. Hosseininia SJ, khalili K, Emam SM. Modal analysis of wind turbine blade using machine vision. Modares Mechanical Engineering. 2016;15 (11):377-386
10. Wahbeh AM, Caffrey JP, Masri SF. A vision-based approach for the direct measurement of displacements in vibrating systems. Smart Materials and Structures. 2003;12(5):785–94.
11. Lee JJ, Shinozuka M. A vision-based system for remote sensing of bridge displacement. NDT & E International. 2006;39(5):425–31.
12. Chang CC, Ji YF. Flexible videogrammetric technique for three-dimensional structural vibration measurement. Journal of Engineering Mechanics. 2007;133(6):656–64.
13. Fleet DJ, Jepson AD. Computation of component image velocity from local phase information. International Journal of Computer Vision. 1990;5(1):77–104.
14. Gautama T, Van Hulle MA. A phase-based approach to the estimation of the optical flow field using spatial filtering. IEEE Transactions on Neural Networks. 2002;13(5):1127-36.
15. Wadhwa N, Rubinstein M, Durand F, Freeman WT. Phase-based video motion processing. ACM Transactions on Graphics (TOG). 2013;32(4):1-0.

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