CLC number: TU192
On-line Access: 2015-02-03
Received: 2014-06-05
Revision Accepted: 2014-10-13
Crosschecked: 2015-01-12
Cited: 0
Clicked: 5954
Citations: Bibtex RefMan EndNote GB/T7714
Xing-huai Huang, Shirley Dyke, Zhao-dong Xu. An in-time damage identification approach based on the Kalman filter and energy equilibrium theory[J]. Journal of Zhejiang University Science A, 2015, 16(2): 105-116.
@article{title="An in-time damage identification approach based on the Kalman filter and energy equilibrium theory",
author="Xing-huai Huang, Shirley Dyke, Zhao-dong Xu",
journal="Journal of Zhejiang University Science A",
volume="16",
number="2",
pages="105-116",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1400163"
}
%0 Journal Article
%T An in-time damage identification approach based on the Kalman filter and energy equilibrium theory
%A Xing-huai Huang
%A Shirley Dyke
%A Zhao-dong Xu
%J Journal of Zhejiang University SCIENCE A
%V 16
%N 2
%P 105-116
%@ 1673-565X
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1400163
TY - JOUR
T1 - An in-time damage identification approach based on the Kalman filter and energy equilibrium theory
A1 - Xing-huai Huang
A1 - Shirley Dyke
A1 - Zhao-dong Xu
J0 - Journal of Zhejiang University Science A
VL - 16
IS - 2
SP - 105
EP - 116
%@ 1673-565X
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1400163
Abstract: In research on damage identification, conventional methods usually face difficulties in converging globally and rapidly. Therefore, a fast in-time damage identification approach based on the kalman filter and energy equilibrium theory is proposed to obtain the structural stiffness, find the locations of damage, and quantify its intensity. The proposed approach establishes a relationship between the structural stiffness and acceleration response by means of energy equilibrium theory. After importing the structural energy into the kalman filter algorithm, unknown parameters of the structure can be obtained by comparing the predicted energy and the measured energy in each time step. Numerical verification on a highway sign support truss with and without damage indicates that the updated Young’s modulus can converge to the true value rapidly, even under the effects of external noise excitation. In addition, the calculation time taken for each step of the approach is considerably shorter than the sampling period (1/256 s), which means that, this approach can be implemented in-time and on-line.
[1]Chatzi, E.N., Smyth, A.W., 2009. The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing. Structural Control & Health Monitoring, 16(1):99-123.
[2]Erdogan, Y.S., Bakir, P.G., 2013. Evaluation of the different genetic algorithm parameters and operators for the finite element model updating problem. Computers and Concrete, 11(6):541-569.
[3]Feng, X.L., He, G.L., Abdurishit, 2008. Estimation of parameters of the Makeham distribution using the least squares method. Mathematics and Computers in Simulation, 77(1):34-44.
[4]Garcia-Perez, A., Amezquita-Sanchez, J.P., Dominguez-Gonzalez, A., et al., 2013. Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 14(9):615-630.
[5]Ghanem, R., Shinozuka, M., 1995. Structural-system identification. I: Theory. Journal of Engineering Mechanics, 121(2):255-264.
[6]Groeneboom, P., Jongbloed, G., Wellner, J.A., 2001. Estimation of a convex function: characterizations and asymptotic theory. Annals of Statistics, 29(6):1653-1698.
[7]Hoshiya, M., Saito, E., 1984. Structural identification by extended Kalman filter. Journal of Engineering Mechanics, 110(12):1757-1770.
[8]Iwasaki, A., Todoroki, A., Shimamura, Y., et al., 2004. Unsupervised structural damage diagnosis based on change of response surface using statistical tool. JSME International Journal Series A, 47(1):1-7.
[9]Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Journal of Fluids Engineering, 82(1):35-45.
[10]Khoo, L.M., Mantena, P.R., Jadhav, P., 2004. Structural damage assessment using vibration modal analysis. Structural Health Monitoring, 3(2):177-194.
[11]Kim, J.T., Ryu, Y.S., Cho, H.M., et al., 2003. Damage identification in beam-type structures: frequency-based method vs mode-shape-based method. Engineering Structures, 25(1):57-67.
[12]Krishnan, S.S., Sun, Z.X., Irfanoglu, A., et al., 2011. Evaluating the performance of distributed approaches for modal identification. Proceeding of SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, San Diego, USA, p.79814M-79814M.
[13]Lee, Y.S., Chung, M.J., 2000. A study on crack detection using eigenfrequency test data. Computers & Structures, 77(3):327-342.
[14]Lei, Y., Wang, H.F., Shen, W.A., 2012. Update the finite element model of canton tower based on direct matrix updating with incomplete modal data. Smart Structures and Systems, 10(4-5):471-483.
[15]Li, T.Y., Zhang, T., Liu, J.X., et al., 2004. Vibrational wave analysis of infinite damaged beams using structure-borne power flow. Applied Acoustics, 65(1):91-100.
[16]Liu, Y., Duan, Z.D., 2012. Fuzzy finite element model updating of bridges by considering the uncertainty of the measured modal parameters. Science China Technological Sciences, 55(11):3109-3117.
[17]Liu, Y., Sun, H., Wang, D.J., 2013. Updating the finite element model of large-scaled structures using component mode synthesis technique. Intelligent Automation and Soft Computing, 19(1):11-21.
[18]López-Díez, J., Torrealba, M., Güemes, A., et al., 2005. Application of statistical energy analysis for damage detection in spacecraft structures. Key Engineering Materials, 293-294:525-532.
[19]Nadauld, J.D., Pantelides, C.P., 2007. Rehabilitation of cracked aluminum connections with GFRP composites for fatigue stresses. Journal of Composites for Construction, 11(3):328-335.
[20]Park, J.S., Stallings, J.M., 2006. Fatigue evaluations of variable message sign structures based on AASHTO specifications. KSCE Journal of Civil Engineering, 10(3):207-217.
[21]Park, N.G., Park, Y.S., 2005. Identification of damage on a substructure with measured frequency response functions. Journal of Mechanical Science and Technology, 19(10):1891-1901.
[22]Sadr, M.H., Astaraki, S., Salehi, S., 2007. Improving the neural network method for finite element model updating using homogenous distribution of design points. Archive of Applied Mechanics, 77(11):795-807.
[23]Shinozuka, M., Ghanem, R., 1995. Structural system-identification. II: experimental-verification. Journal of Engineering Mechanics, 121(2):265-273.
[24]Sinha, J.K., Friswell, M.I., 2003. The use of model updating for reliable finite element modelling and fault diagnosis of structural components used in nuclear plants. Nuclear Engineering and Design, 223(1):11-23.
[25]Song, W., Dyke, S., 2014. Real-time dynamic model updating of a hysteretic structural system. Journal of Structural Engineering, 140(3):04013082.
[26]van der Merwe, R., Wan, E., Julier, S.J., 2004. Sigma-point Kalman filters for nonlinear estimation and sensor fusion: applications to integrated navigation. Proceedings of the AIAA Guidance Navigation & Control Conference, Providence, Rhode Island, USA, p.1735-1764.
[27]Xu, Z.D., Wu, Z.S., 2007. Energy damage detection strategy based on acceleration responses for long-span bridge structures. Engineering Structures, 29(4):609-617.
[28]Xu, Z.D., Liu, M., Wu, Z.S., et al., 2011. Energy damage detection strategy based on strain responses for long-span bridge structures. Journal of Bridge Engineering, 16(5):644-652.
[29]Yan, A.M., Golinval, J.C., 2005. Structural damage localization by combining flexibility and stiffness methods. Engineering Structures, 27(12):1752-1761.
[30]Yan, G.R., Dyke, S.J., Irfanoglu, A., 2012. Experimental validation of a damage detection approach on a full-scale highway sign support truss. Mechanical Systems and Signal Processing, 28:195-211.
[31]Yang, J.N., Lin, S.L., Huang, H.W., et al., 2006. An adaptive extended Kalman filter for structural damage identification. Structural Control & Health Monitoring, 13(4):849-867.
[32]Zhao, X., Sun, H.H., Zheng, Y.M., 2009. Identification and updating for the three-dimensional finite element model of a long span steel skybridge. Structural Design of Tall and Special Buildings, 18(6):625-646.
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