Browse > Article
http://dx.doi.org/10.7734/COSEIK.2019.32.1.45

Damage Detection of Building Structures using AEKF(Adaptive Extended Kalman Filter)  

Yun, Da Yo (Department of Architectural Engineering, Yonsei Univ.)
Kim, Yousok (Department of Architectural Engineering, Hongik University)
Park, Hyo Seon (Department of Architectural Engineering, Yonsei Univ.)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.32, no.1, 2019 , pp. 45-54 More about this Journal
Abstract
The damage detection method using the extended Kalman filter(EKF) technique has been continuously used since EKF can estimation the responses of the damaged building structure and the stiffness of the structure. However, in the use of EKF, the requirement of setting the initial paramters P, Q, and R has caused the divergence and instability of the state vector, and various researches have been conducted to determine theses parameters. In this paper, adaptive extended Kalman filter(AEKF) method is proposed to solve the problem of setting the values of P, Q, and R, which are important parameters determining the convergence performance of the EKF state vector. By using the AEKF method proposed in this study, the P, Q, and R parameters are updated every k steps. The proposed algorithm is applied for the estimation of stiffness and the damage detection of 3-DOF problem. Based of the verification, it can be found that the selection process for the values of P, Q, and R can improve the convergence performance of EKF.
Keywords
adaptive extended kalman filter(AEKF); damage detection; stiffness estimation; building structures;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ghorbani, E., Cha, Y.J. (2018), An Iterated Cubature Uncscented Kalman Filter for Large-DoF Systems Identification with Noisy Data, J. Sound & Vib., 420, pp.21-34.   DOI
2 Hoshiya, M., Saito, E. (1984) Structural Identification by Extended Kalman Filtert, J. Eng. Mech., 110(12), pp.1757-1770.   DOI
3 Hernandez, E.M. (2013) Optimal Model-based State Estimation in Mechanical and Structural Systems, Struct. Control & Health Monit., 20, pp.532-543.   DOI
4 Jazwinski, A.H. (1970) Stochastic Process and Filtering Theory, Academic Press, New York
5 Kim, D.Y., Oh, B.K., Park, H.S. (2017) Modal Ifentification for High-Rise Building Structures using Orthogonality of Filtered Response Vector, Computer-Aided Civil & Infrastruct. Eng., 32, pp.1064-1084.   DOI
6 Lei, Y., Liu., Liu, L.J. (2014), Identification of Multistory Shear Buildings under Unknown Earthquake Excitation using Partial Output Measurements: Numerical and Experimental Studies, Struct. Control & Health Monit., 21, pp.774-784.   DOI
7 Lin, J.S., Zhang, Y. (1994) Nonlinear Structural Identification using Extended Kalman Filter, Comput. & Struct., 52(4), pp.757-764.   DOI
8 Ming, G., Kerrigan, E.C. (2017) Noise Covariance Identification for Time-Varing and Nonlinear Systems, Int. J. Control, 90(9), pp.1903-1915.   DOI
9 Odelson, B.J., Rajamani, M.R., Rawlings, J.B. (2006) A New Autocovariance Least-Squares Method for Estimating Noise Covariances, Automatica, 42(2), pp.303-308.   DOI
10 Oh, B.K., Kim, D.Y., Park, H.S. (2017) Modal Response-Based Visual System Identification and Model Updating Methods for building Structures, Computer-Aided Civil & Infrastruct. Eng., 32, pp.34-56.   DOI
11 Oh, B.K., Kim, J.H., Park, H.S. (2019) Model Updating Method for Damage Detection of Building Structures Under Ambient Excitation using Modal Participation Ratio, Measurement, 133, pp.251-261.   DOI
12 Park, H,S., Oh, B.K. (2018) Damage Detection of Building Structures under Ambient Excitation through the Analysis of the Relationship between the Modal Participation Ratio and Story Stiffness, J. Sound & Vib., 418, pp.122-143.   DOI
13 Schneider, R., Georgakis, C. (2013) How to not Make the Rxtended Kalman Filter Fail, Industrial & Eng. Chem. Res., 52, pp.3354-3362.   DOI
14 Solonen, A., Hakkarainen, J., Ilin, A. Abbas, M., Bibov, A. (2014), Estimating Model Error Covariance Matrix Parameters in Extended Kalmna Filtering, Nonlinear Proc. Geophys., 21, pp.919-927.   DOI
15 Wang, D., Haldar, A. (1997) System Identification with Limited Observations and Without Input, J. Eng. Mech., 123(5), pp.504-510.   DOI
16 Wang, J., Wang, J., Zhang, D., Shao, X., Chen, G. (2018) Kalman Filtering through the Feedback Adaption of Prior Error Covariance, Signal Proc., 152, pp.47-53.   DOI
17 Zhang, C., Huang, J.Z., Song, G.Q., Chen, L. (2017) Structural Damage Identification by Extended Kalman Filter with l1-norm Regularization Scheme Structural System Identification, Struct. Control & Health Monit., 24(11), e1999.   DOI
18 Julier, S.J., Uhlmann J.K. (2004) Unsented Filtering and Nonlinear Estimation, Proc. IEEE, 92(3), pp.401-422.   DOI
19 Wu, M., Smyth, A.W. (2007) Application of the Unscented Kalman Filter for Real-Time Nonlinear Structural System Identification, Struct. Control & Health Monit., 14, pp.971-990.   DOI