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Crack identification in composite elements with non-linear geometry using spatial wavelet transform

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Języki publikacji
EN
Abstrakty
EN
A great popularity of constructional polymer composites in industrial applications requires the development of appropriate diagnostic methods for the elements made of these materials. These methods should provide damage detection and identification in possible early stages of its development. A large group of such methods is based on vibration testing and modal analysis. One of the promising techniques of processing vibration data for damage identification is a wavelet transform. Considering the practical needs the method should be applicable for plane structures with non-linear geometry. Results presented in this paper consider numerous analyses including the application of different types of wavelet transforms, different types of wavelet functions and other factors, which have an influence on the accuracy of damage identification. The proposed method shows good effectiveness in damage identification problems and could be applied in industrial conditions as well.
Rocznik
Strony
287--296
Opis fizyczny
Bibliogr. 26 poz., wykr.
Twórcy
autor
  • Silesian University of Technology, Institute of Fundamentals of Machinery Design, 18A Konarskiego Str., 44-100 Gliwice, Poland, andrzej.katunin@polsl.pl
autor
  • Silesian University of Technology, Institute of Mechanics and Computational Engineering, 18A Konarskiego Str., 44-100 Gliwice, Poland
Bibliografia
  • [1] J.T. Białasiewicz, Wavelets and Approximations (in Polish), WNT, Warsaw, 2000.
  • [2] C.C. Chang, L.W. Chen, Damage detection of a rectangular plate by spatial wavelet based approach, Applied Acoustics 65 (2004) 819–832.
  • [3] W. Fan, P. Qiao, A 2-D continuous wavelet transform of mode shape data for damage detection of plate structures, International Journal of Solids and Structures 46 (2009) 4379–4395.
  • [4] M. Fedi, F. Cella, T. Quarta, A.V. Villani, 2D continuous wavelet transform of potential fields due to extended source distributions, Applied and Computational Harmonic Analysis 28 (2010) 320–337.
  • [5] H. Gokdag, O. Kompaz, A new damage detection approach for beam-type structures based on the combination of continuous and discrete wavelet transform, Journal of Sound and Vibration 324 (2009) 1158–1180.
  • [6] M. Holschneider, R. Kronland-Martinet, J. Morlet, P. Tchamitchian, A real-time algorithm for signal analysis with the help of the wavelet transform, in: Jean-Michel Combes, Alexander Grossman, Philippe Tchamitchian(Eds.), Wavelets, Time-Frequency Methods and Phase Space, Springer-Verlag, 1989, pp. 289–297.
  • [7] Y. Huang, D. Meyer, S. Nemat-Nasser, Damage detection with spatially distributed 2D continuous wavelet transform, Mechanics of Materials 41 (2009) 1096–1107.
  • [8] A. Katunin, Identification of multiple cracks in composite beams using discrete wavelet transform, Scientific Problems of Machines Operation and Maintenance 45 (2010) 41–52.
  • [9] A. Katunin, The construction of high-order B-spline wavelets and their decomposition relations for fault detection and localisation in composite beams, Scientific Problems of Machines Operation and Maintenance 46 (2011) 43–59.
  • [10] A. Katunin, Damage identification in composite plates using two-dimensional B-spline wavelets, Mechanical Systems and Signal Processing 25 (2011) 3153–3167.
  • [11] A. Katunin, Solution of plane Dirichlet problem using compactly supported 2D wavelet scaling functions, Scientific Research of the Institute of Mathematics and Computer Science 11 (2012) 31–40.
  • [12] M. Lakestani, M. Dehghan, The solution of a second-order nonlinear differential equation with Neumann boundary conditions using semi-orthogonal B-spline wavelets, International Journal of Computer Mathematics 83 (2006) 685–694.
  • [13] S. Loutridis, E. Douka, L.J. Hadjileontiadis, A. Trochidis, A two-dimensional wavelet transform for detection of cracks in plates, Engineering Structures 27 (2005) 1327–1338.
  • [14] S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, pp. 674–693.
  • [15] P. Mularczyk, Modeling of problems of active damping of vibration of a chosen object (in Polish), Master Thesis, Gliwice, 2007.
  • [16] G.P. Nason, B.W. Silverman, The stationary wavelet transform and some statistical applications, Lecture Notes in Statistics 103 (1995) 281–299.
  • [17] J.-C. Pesquet, H. Krim, H. Carfantan, Time-invariant orthonormal wavelet representations, IEEE Transactions on Signal Processing 44 (1996) 1964–1970.
  • [18] J.M. Rees, G. Regunath, S.P. Whiteside, M.B. Wanderkar, P.E. Cowell, Adaptation of wavelet transform analysis to the investigation of biological variations in speech signals, Medical Engineering & Physics 30 (2008) 865–871.
  • [19] M. Rucka, K. Wilde, Application of continuous wavelet transform in vibration based damage detection method for beams and plates, Journal of Sound and Vibration 297 (2006) 536–550.
  • [20] J. Schmeelk, Wavelet transforms and edge detectors on digital images, Mathematical and Computer Modelling 41 (2005) 1469–1478.
  • [21] J. Shang, Y. He, D. Liu, H. Zang, W. Chen, Laser Doppler vibrometer for real-time speech-signal acquirement, Chinese Optics Letters 7 (2009) 732–733.
  • [22] W. Sweldens, The lifting scheme: a construction of second generation wavelets, SIAM Journal on Mathematical Analysis 29 (1998) 511–546.
  • [23] M. Unser, A. Aldroubi, M. Eden, On the asymptotic convergence of B-spline wavelets to Gabor functions, IEEE Transactions on Information Theory 38 (1992) 864–872.
  • [24] Q. Wang, X. Deng, Damage detection with spatial wavelets, International Journal of Solids and Structures 36 (1999) 3442–3468.
  • [25] Y. Wu, Y. He, H. Cai, Optimal threshold selection algorithm in edge detection based on wavelet transform, Image and Vision Computing 23 (2005) 1159–1169.
  • [26] S. Zhong, S.O. Oyadiji, Crack detection in simply supported beams without baseline modal parameters by stationary wavelet transform, Mechanical Systems and Signal Processing 21 (2007) 1853–1884.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3da5414d-c69e-4ba6-be2c-ff97b6ac4af9
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