Image-based experimental modal analysis
Image-based experimental modal analysis is a nondestructive measurement technique for modal testing of mechanical structures, using digital images as the main data source. Displacements of the observed structure are identified in digital images, often using digital image correlation (DIC) [1], gradient-based optical flow [2] or other photogrammetric methods. State-of-the-art image-based displacement identification techniques can achieve intra-frame resolution at the level of 1×10−2 pixel, and can identify harmonic oscillations with amplitudes as low as 1×10−4 pixel due to noise dithering, enabling vibration measurements even at high frequencies.[3]. This displacement data can then used for analysis of the structure's dynamic properties [3] [4] [5]. To enable high-frequency vibration measurement, high-speed cameras are normally used for image acquisition, although other imaging techniques can be used as well [6]
Modal identification using image-based data[edit]
The mechanical structure's modal properties - natural frequencies, damping ratios and mode shapes - are often identified from the image-based displacement data, transformed into frequency domain. One of the main advantages of using image-based displacement data for modal identification is the high spatial resolution of mode shapes, typically resulting from image-based measurements. Various modal identification techniques exist, and the choice of which one to use depends on the nature of the measurement.
The Eigensystem realization algorithm (ERA) was one of the first modal identification techniques to be used with photogrammetrically acquired data, to determine the modal properties of the Mir space station solar array [7].
The frequency domain decomposition (FDD) is a modal identification technique that has been used on image-based displacement data [8], and is often used in civil engineering for structural health monitoring, due to its output-only nature. Blind source separation (BSS) is another technique that can be used together with phase-based motion magnification [9] to identify modal parameters of a vibrating structure without measuring the input (excitation) signal.
In mechanical engineering, the Least-squares complex frequency-domain method (LSCF).[10] is frequently used, and can operate on image-based displacement data. A hybrid modal identification technique exists, that uses accelerometer data to identify the eigenvalues in LSCF, and image-based data to identify the mode shapes with high spatial resolution, and even below the camera measurement's noise floor. Using this technique, modal analysis of harmonically oscillated structures can be performed at the level of 1×10−5 pixel [11]
See also[edit]
- Vibration
- Structural dynamics
- Modal analysis
- Modal testing
- Digital image correlation and tracking
- Optical flow
- Nondestructive testing
References[edit]
- ↑
Sutton, M. A.; Orteu, J. J.; Schreier, H. (2009). Image correlation for shape, motion and deformation measurements: basic concepts, theory and applications. Springer US. doi:10.1007/978-0-387-78747-3. ISBN 978-0-387-78747-3. Search this book on
- ↑ Horn, Berthold K.P.; Schunck, Brian G. (1981). "Determining optical flow" (PDF). Artificial Intelligence. 17 (1–3): 185–203. doi:10.1016/0004-3702(81)90024-2.
- ↑ 3.0 3.1 Javh, Jaka; Slavič, Janko; Boltežar, Miha (2017). "The subpixel resolution of optical-flow-based modal analysis". Mechanical Systems and Signal Processing. 88: 89–99. Bibcode:2017MSSP...88...89J. doi:10.1016/j.ymssp.2016.11.009.
- ↑ Helfrick, Mark; Niezrecki, Christopher; Avitabile, Peter; Schmidt, Timothy (2011). "3D digital image correlation methods for full-field vibration measurement". Mechanical Systems and Signal Processing. 25 (3): 917–927. Bibcode:2011MSSP...25..917H. doi:10.1016/j.ymssp.2010.08.013.
- ↑ Baqersad, Javad; Poozesh, Peyman; Niezrecki, Christopher; Avitabile, Peter (2017). "Photogrammetry and optical methods in structural dynamics – A review". Mechanical Systems and Signal Processing. 86: 17–34. Bibcode:2017MSSP...86...17B. doi:10.1016/j.ymssp.2016.02.011.
- ↑ Javh, Jaka; Slavič, Janko; Boltežar, Miha (2018). "Experimental Modal Analysis on Full-Field DSLR Camera Footage Using Spectral Optical Flow Imaging". Journal of Sound and Vibration. 434: 213–220. Bibcode:2018JSV...434..213J. doi:10.1016/j.jsv.2018.07.046.
- ↑
G. Gilbert, Michael; Welch, Sharon; Pappa, Richard; E. Demeo, Martha (1997). "STS-74/MIR Photogrammetric Appendage Structural Dynamics Experiment Preliminary Data Analysis" (PDF). 38th Structures, Structural Dynamics, and Materials Conference. doi:10.2514/6.1997-1168. hdl:2060/19970015406. Search this book on
- ↑ Oh, Byung Kwan; Hwang, Jin Woo; Kim, Yousok; Tongjun, Cho; Park, Hyo Seon (2015). "Vision-based system identification technique for building structures using a motion capture system". Journal of Sound and Vibration. 356: 72–85. Bibcode:2015JSV...356...72O. doi:10.1016/j.jsv.2015.07.011.
- ↑ Yang, Yongchao; Dorn, Charles; Mancini, Tyler; Talken, Zachary; Kenyon, Garrett; Farrar,Charles; Mascareñas, David (2017). "Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification". Mechanical Systems and Signal Processing. 85: 567–590. Bibcode:2017MSSP...85..567Y. doi:10.1016/j.ymssp.2016.08.041.
- ↑ Guillaume, Patrick; Verboven, Peter; Vanlanduit, S.; Van der Auweraer, Herman; Peeters, Bart (2003). "A poly-reference implementation of the least-squares complex frequency-domain estimator". Proceedings of IMAC. 21.
- ↑ Javh, Jaka; Slavič, Janko; Boltežar, Miha (2018). "High frequency modal identification on noisy high-speed camera data". Mechanical Systems and Signal Processing. 98: 344–351. Bibcode:2018MSSP...98..344J. doi:10.1016/j.ymssp.2017.05.008.
This article "Image-based experimental modal analysis" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Image-based experimental modal analysis. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.
