Structural Damage Identification Using Wavelet Packet Analysis and Neural Network

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This paper describes a study on damage identification using wavelet packet analysis and neural networks. The identification procedure could be divided into three steps. First, structure responses are decomposed into wavelet packet components. Then, the component energies are used to define damage feature and to train neural network models. Finally, in combination with the feature of the damaged structure response, the trained models are employed to determine the occurrence, the location and the qualification of the damage. The emphasis of this study is put on multi-damage case. Relevant issues are studied in detail especially the selection of training samples for multi-damage identification oriented neural network training. A frame model is utilized in the simulation cases to study the sampling techniques and the multi-damage identification. Uniform design is determined to be the most suitable sampling technique through simulation results. Identifications of multi-damage cases of the frame including different levels of damage at various locations are investigated. The results show that damages are successfully identified in all cases.

Info:

Periodical:

Key Engineering Materials (Volumes 324-325)

Edited by:

M.H. Aliabadi, Qingfen Li, Li Li and F.-G. Buchholz

Pages:

205-208

DOI:

10.4028/www.scientific.net/KEM.324-325.205

Citation:

Q. G. Fei et al., "Structural Damage Identification Using Wavelet Packet Analysis and Neural Network", Key Engineering Materials, Vols. 324-325, pp. 205-208, 2006

Online since:

November 2006

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Price:

$38.00

[1] S.W. Doebling, C.R. Farrar and M.B. Prime: A review of damage identification methods that examine changes in dynamic properties, Shock and Vibration Digest, Vol. 30(1998), pp.91-105.

DOI: 10.1177/058310249803000201

[2] E. Douka, S. Loutridis and A. Trochidis: Crack identification in beams using wavelet analysis, International Journal of Solids and Structures, Vol. 40(2003), pp.3357-3569.

DOI: 10.1016/s0020-7683(03)00147-1

[3] J.G. Han, W.X. Ren and Z.S. Sun: Wavelet packet based damage identification of beam structures, International Journal of Solids and Structures, Vol. 42(2005), pp.6610-6627.

DOI: 10.1016/j.ijsolstr.2005.04.031

[4] G.G. Yen and K.C. Lin: Wavelet packet feature extraction for vibration monitoring, IEEE Trans. On Ind. Electron, Vol. 47(2000), pp.650-667.

DOI: 10.1109/41.847906

[5] Z. Sun and C.C. Chang: Structural damage assessment based on wavelet packet transform, Journal of Structural Engineering, Vol. 128(2002), pp.1354-1361.

DOI: 10.1061/(asce)0733-9445(2002)128:10(1354)

[6] R.J. Levin, N.A.J. Lieven: Dynamic finite element model updating using neural networks, Journal of Sound and Vibration, Vol. 210(1998), pp.593-607.

DOI: 10.1006/jsvi.1997.1364

[7] Douglas C. Montgomery: Design and analysis of experiments, New York: Wiley (2003).

[8] K.T. Fang and C.X. Ma: Orthogonal and uniform experimental design, Science Press (2001).

[9] S. Chen, C.F.N. Cowan and P.M. Grant: Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks. Vol. 2(1991), pp.302-309.

DOI: 10.1109/72.80341

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