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 ENG  Vol.12 No.10 , October 2020
An Optimized Damage Identification Method of Beam Using Wavelet and Neural Network
Abstract: An optimized damage identification method of beam combined wavelet with neural network is presented in an attempt to improve the calculation iterative speed and accuracy damage identification. The mathematical model is developed to identify the structure damage based on the theory of finite elements and rotation modal parameters. The model is integrated with BP neural network optimization approach which utilizes the Genetic algorithm optimization method. The structural rotation modal parameters are performed with the continuous wavelet transform through the Mexico hat wavelet. The location of structure damage is identified by the maximum of wavelet coefficients. Then, the multi-scale wavelet coefficients modulus maxima are used as the inputs of the BP neural network, and through training and updating the optimal weight and threshold value to obtain the ideal output which is used to describe the degree of structural damage. The obtained results demonstrate the effectiveness of the proposed approach in simultaneously improving the structural damage identification precision including the damage locating and severity.
Cite this paper: Miao, B. , Wang, M. , Yang, S. , Luo, Y. and Yang, C. (2020) An Optimized Damage Identification Method of Beam Using Wavelet and Neural Network. Engineering, 12, 748-765. doi: 10.4236/eng.2020.1210053.
References

[1]   Fan, W. and Qiao, P.Z. (2009) A 2-D Continuous Wavelet Transform of Mode Shape Data for Damage Detection Of Plate Structures. International Journal of Solids and Structures, 46, 4379-4395.
https://doi.org/10.1016/j.ijsolstr.2009.08.022

[2]   Gkdağ, H. and Kopmaz, O. (2009) A New Damage Detection Approach FOR Beam-Type Structures Based on the Combination of Continuous and Discrete Wavelet Transforms. Journal of Sound and Vibration, 324, 1158-1180.
https://doi.org/10.1016/j.jsv.2009.02.030

[3]   Xu, W., Radzieński, M., Ostachowicz, W. and Cao, M. (2013) Damage Detection in Plates Using Two-Dimensional Directional Gaussian Wavelets and laser Scanned Operating Deflection Shapes. Structural Health Monitoring, 12, 457-468.
https://doi.org/10.1177/1475921713492365

[4]   Le, T.-P. and Paultre, P. (2012) Modal Identification Based on Continuous Wavelet Transform and Ambient Excitation Tests. Journal of Sound and Vibration, 331, 2023-2037.
https://doi.org/10.1016/j.jsv.2012.01.018

[5]   Mehrjoo, M., Khaji, N., Moharrmi, H. and Bahreininejad, A. (2008). Damage Detection of Truss Bridge Joints Using Artificial Neural Networks. Expert Systems with Applications, 35, 1122-1131.
https://doi.org/10.1016/j.eswa.2007.08.008

[6]   Liu, S.W., Huang, J.H., Sung, J.C. and Lee, C.C. (2002) Detection of Cracks Using Neural Networks and Computational Mechanics. Computer Methods in Applied Mechanics and Engineering, 191, 2831-2845.
https://doi.org/10.1016/S0045-7825(02)00221-9

[7]   Yam, L.H., Yan, Y.J. and Jiang, J.S. (2003). Vibration-Based Damage Detection for Composite Structures Using Wavelet Transform and Neural Network Identification. Composite Structures, 60, 403-412.
https://doi.org/10.1016/S0263-8223(03)00023-0

[8]   Lu, Y., Ye, L., Su, Z.Q. and Yang, C.H. (2008) Quantitative Assessment of Through-Thickness Crack Size Based on Lamb Wave Scattering in Aluminium Plates. Ndt & E International, 41, 59-68.
https://doi.org/10.1016/j.ndteint.2007.07.003

[9]   Adeli, H. and Jiang, X. (2006) Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification. Journal of Structural Engineering, 132, 102-111.
https://doi.org/10.1061/(ASCE)0733-9445(2006)132:1(102)

[10]   Liu, Y.Y., Ju, Y.F., Duan, C.D. and Zhao, X.F. (2011) Structure Damage Diagnosis Using Neural Network and Feature Fusion. Engineering Applications of Artificial Intelligence, 24, 87-92.
https://doi.org/10.1016/j.engappai.2010.08.011

[11]   Gao, Z.-F. and Chen, X.-J. (2011) Structure Data Processing and Damage Identification Based on Wavelet and Artificial Neural Network. Research Journal of Applied Sciences, Engineering and Technology, 3, 1203-1208.

[12]   Sahoo, B. and Maity, D. (2007) Damage Assessment of Structures Using Hybrid Neuro-Genetic Algorithm. Applied Soft Computing, 7, 89-104.
https://doi.org/10.1016/j.asoc.2005.04.001

[13]   Vakil-baghmisheh, M.T., Peimani, M., Sadeghi, M.H. and Ettefagh, M.M. (2008) Crack Detection in Beam-Like Structures Using Genetic Algorithms. Applied Soft Computing, 8, 1150-1160.
https://doi.org/10.1016/j.asoc.2007.10.003

[14]   Buezas, F.S., Rosales, M.B. and Filipich, C.P. (2011) Damage Detection with Genetic Algorithms Taking into Account a Crack Contact Model. Engineering Fracture Mechanics, 78, 695-712.
https://doi.org/10.1016/j.engfracmech.2010.11.008

[15]   Meruane, V. and Heylen, W. (2011) An Hybrid Real Genetic Algorithm to Detect Structural Damage Using Modal Properties. Mechanical Systems and Signal Processing, 25, 1559-1573.
https://doi.org/10.1016/j.ymssp.2010.11.020

[16]   Hao, H. and Xia, Y. (2002) Vibration-Based Damage Detection of Structures by Genetic Algorithm. Journal of Computing in Civil Engineering, 16, 222-229.
https://doi.org/10.1061/(ASCE)0887-3801(2002)16:3(222)

[17]   Miao, B.R., Yang, S.W., Wang M.Y., Jiang, C.Y., Peng, Q.M. and Luo Y.X. (2020) Comparison of Various Structural Damage Identification Methods Using Vibration Response. Journal of Vibration Engineering, 33, 724-733. (in Chinese)

[18]   Yang, C. and Wang, Z. (2010) Application Research on Genetic Algorithm and BP Neural Network in Motor Fault Diagnosis. Noise and Vibration Control, 5, 153-156. (in Chinese)

[19]   Ren, Q.W., Qiu, Y. and Ye, H.J. (2004) Structural Damage Detection Based on Wavelet Transform and Neural Network. Journal of Hohai University: Natural Sciences, 32, 295-299. (in Chinese)

[20]   Wu, D.S. and Liang, L. (2004) A Strategy of Optimizing Neural Networks by Genetic Algorithm and Its Application on Credit Scoring. Chinese Journal of Management Science, 1. (in Chinese)

[21]   Guan, D.Q. and Jiang, X. (2007) Damage Identification of Continuous Beam Based on Wavelet Transform of Rotation Mode. Journal of Changsha University of Science & Technology (Natural Science), 4. (in Chinese)

[22]   Hong, J.C., Kim, Y.Y., Lee, H.C. and Lee, Y.W. (2002) Damage Detection Using the Lipschitz Exponent Estimated by the Wavelet Transform: Applications to Vibration Modes of a Beam. International Journal of Solids and Structures, 39, 1803-1816.
https://doi.org/10.1016/S0020-7683(01)00279-7

 
 
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