Identification of structural parameters and unknown excitations based on the extended kalman filter

被引：0
|
作者：
Zhang X.-X. [1 ]
He J. [1 ]
机构：
[1] College of Civil Engineering, Hunan University, Hunan Provincial Key Lab on Damage Diagnosis for Engineering Structures, Changsha, 410082, Hunan
来源：
Gongcheng Lixue/Engineering Mechanics | 2019年 / 36卷 / 04期
关键词：
Extended Kalman filter; Least squares estimation; Linear and nonlinear structural parameter identification; Projection matrix; Unknown external excitation;
D O I：
10.6052/j.issn.1000-4750.2018.03.0139
中图分类号：
学科分类号：
摘要：
The classical extended Kalman filter (EKF) method is capable of accurately identifying structural parameters with known external excitations. However, in some practical situations, the excitations are difficult or impossible to measure. A time-domain approach based on EKF is proposed in this paper for the simultaneous identification of structural parameters and unknown inputs. A projection matrix is introduced in the observation equation, based on which the structural parameters are identified. The unknown inputs are determined by means of least squares estimation using the estimated parameters. The effectiveness and robustness of the proposed approach is verified through three numerical examples including a four-story benchmark model, a piecewise linear structure and a Duffing hysteretic structure. The numerical results show that the proposed approach can not only accurately identify the parameters of linear and nonlinear structures, but also satisfactorily estimate the unknown external excitations. © 2019, Engineering Mechanics Press. All right reserved.
引用
收藏
页码：221 / 230
页数：9
相关论文
共 18 条
  • [1] Swagato D., Saha P., Patro S., Vibration-based damage detection techniques used for health monitoring of structures: a review, Journal of Civil Structural Health Monitoring, 6, 3, pp. 477-507, (2016)
  • [2] Li H., Bao Y., Li S., Et al., Data science and engineering for structural health monitoring, Engineering Mechanics, 32, 8, pp. 1-7, (2015)
  • [3] Xu B., He J., Rovekamp R., Et al., Structural parameters and dynamic loading identification from incomplete measurements: approach and validation, Mechanical Systems and Signal Processing, 28, pp. 244-257, (2012)
  • [4] Mei Z., Wu B., Yang G., Online parameter identification of concrete constitutive model, Engineering Mechanics, 33, 7, pp. 108-115, (2016)
  • [5] Yang J.N., Huang H., Lin S., Sequential nonlinear least square estimation for damage identification of structures, International Journal of Non-Linear Mechanics, 41, 1, pp. 124-140, (2006)
  • [6] Wu M., Smyth A.W., Application of the unscented Kalman filter for real-time nonlinear structural system identification, Structural Control and Health Monitoring, 14, 7, pp. 971-990, (2007)
  • [7] Lei Y., Chen F., Zhou H., An algorithm based on two-step Kalman filter for intelligent structural damage detection, Structural Control and Health Monitoring, 22, 4, pp. 694-706, (2015)
  • [8] Zhang C., Chen L., Song G., Et al., Structural damage identification by unscented kalman filter with l1 regularization, Engineering Mechanics, 38, 8, pp. 76-84, (2017)
  • [9] Jin C., Jang S., Sun X., An integrated real-time structural damage detection method based on extended Kalman filter and dynamic statistical process control, Advances in Structural Engineering, 20, 4, pp. 549-563, (2017)
  • [10] Zhang C., Huang J.Z., Song G.Q., Et al., Structural damage identification by extended Kalman filter with l1-norm regularization scheme, Structural Control and Health Monitoring, 24, 11, pp. 1-17, (2017)
12