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Visualization of vibrations in structural diagnoses of technical objects

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Języki publikacji
EN
Abstrakty
EN
One of the key methods for diagnosing the structural degradation of technical objects relies on observations of mechanical vibrations that accompany equipment operation and damage. Hardware and software advancements and the development mathematical methods for modelling and inference have increased the popularity of vibroacoustic diagnostics in mechanical systems. Displacement in the time domain of physical points in a vibrating object is the primary diagnostic symptom that undergoes further processing in the measurement system. At present, vibrations are usually registered with the use of accelerometers or optical sensors. Advanced tools for image recording, processing and analysis are deployed in quasi-realistic observations of motion that cannot be perceived by the human senses. This article discusses a method for visualizing vibrations based on deliberate deformation of the registered image through motion magnification. The presented approach is illustrated with selected examples.
Rocznik
Strony
art. no. 2019205
Opis fizyczny
Bibliogr. 19 poz., rys., wykr.
Twórcy
  • University of Warmia and Mazury in Olsztyn, Faculty of Technical Sciences, Oczapowskiego str. 11, 10-957 Olsztyn, Poland
  • University of Warmia and Mazury in Olsztyn, Faculty of Technical Sciences, Oczapowskiego str. 11, 10-957 Olsztyn, Poland
  • University of Warmia and Mazury in Olsztyn, Faculty of Technical Sciences, Oczapowskiego str. 11, 10-957 Olsztyn, Poland
  • I-Care Polska Sp. z o.o., ul. Puszkarska 9, 30-644 Kraków, Poland
  • I-Care Polska Sp. z o.o., ul. Puszkarska 9, 30-644 Kraków, Poland
Bibliografia
  • 1. B. Zoltowski, M. Zoltowski, Selection measure of energy propagation in vibration diagnostic and modal analysis methods, Diagnostyka, 19(4) (2018) 19 - 26.
  • 2. M. Azzouz, R. Diarra, D. Popescu. Fault diagnosis of sensors, actuators and wind turbine system, Diagnostyka, 19(4) (2018) 3 - 10.
  • 3. N. Wadhwa, J. G. Chen, J. B. Sellon, D. Wei, M. Rubinstein, Roozbeh Ghaffari, Dennis M. Freeman, Oral Büyüköztürk, Pai Wang, Sijie Sun, S. H. Kang, K. Bertoldi, F. Durand, W. T. Freeman, Motion microscopy for visualizing and quantifying small motions, PNAS, 114(44) (2017) 11639 - 11644.
  • 4. J. L. Barron, D. J. Fleet, S. S. Beauchemin, Performance of optical flow techniques, International Journal of Computer Vision, 12(1) (1994) 43 - 77.
  • 5. Ce Liu, Antonio Torralba William T. Freeman Frédo Durand Edward H. Adelson, Motion magnification, ACM Transactions on Graphics, 24(3) (2005) 519 - 526.
  • 6. J. Y. A. Wang, E. H. Adelson, Representing moving images with layers. IEEE Transactions on Image Processing, 3(5) 1994.
  • 7. A. A. Efros, T. K. Leung, Texture synthesis by non-parametric sampling, Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE. 1999.
  • 8. W. Hao-Yu, M. Rubinstein, E. Shih, J. Guttag, F. Durand, W. Freeman, Eulerian video magnification for revealing subtle changes in the world, ACM Transactions on Graphics, 31(4) (2012) 1 - 8.
  • 9. E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, J. M. Ogden, Pyramid methods in image processing. RCA Engineer, 29(6) (1984) 33 - 41.
  • 10. J. G. Chen Neal, W. Young-Jin Cha, F. Durand, WT. F. O. Buyukozturk, Modal identification of simple structures with high-speed video using motion magnification, Journal of Sound and Vibration, 345(9) (2015) 58 - 71.
  • 11. A. Karasaridis, E. P. Simoncelli, A filter design technique for steerable pyramid image transforms, IEEE International Conference on Acoustics. 1996.
  • 12. N. Wadhwa, M. Rubinstein, F. Durand, W. Freeman, Phase-based Video Motion Processing, ACM Trans. Graph, 32(4) (2013).
  • 13. N. Wadhwa, M. Rubinstein, F. Durand, W. T. Freeman, Riesz pyramids for fast phase-based video magnification, IEEE International Conference on Computational Photography (ICCP) (2014).
  • 14. L. Sarode, N. N. Mandaogade, Review on Video Motion Magnification, International Journal of Advance Research in Computer Science and Management Studies, 2(1) (2014) 480 - 484.
  • 15. K. Kamble, N. Jagtap, R. A. Patil, A. Bhurane, A review: Eulerian video motion magnification, International Journal of Innovative Research in Computerand Communication Engineering, 3(3) (2015) 2384 - 2390.
  • 16. M. Elgharib, M. Hefeeda, F. Durand, W. T. Freeman, Video magnification in presence of large motions, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015) 4119 - 4127.
  • 17. M. Verma, S. Raman, Interest region based motion magnification, Lecture Notes in Computer Science, 10484 (2017) 27 - 39.
  • 18. J. Kooij, J. C. van Gemert, Depth-aware motion magnification, European Conference on Computer Vision, (2016) 467 - 482.
  • 19. S. Takeda, Y. Akagi, K. Okami, M. Isogai, H. Kimata, Video magnification in the wild using fractional anisotropy in temporal distribution, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2019) 1614 - 1622.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-011678ef-00f6-46d5-a0dc-5347e571f414
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