On-line Damage Identification of Structural System Based on Adaptive Extended Kalman Filter
Date Issued
2007
Date
2007
Author(s)
Huang, Shieh-Kung
DOI
en-US
Abstract
Structural health monitoring (SHM) received considerable attention in civil engineering. To realize this, system identification and damage detection becomes the most important technique in the last ten or fifteen years. During a severe dynamic event, the structural system may suffer certain degree of damage. The damage of the structural system will be reflected by the variations of parametric value, and contained in the response measurements. Therefore, system identification techniques, especially on-line identification techniques, are commonly used recently.
For the on-line identification techniques, time domain analysis has been applied with iterative computation, such as Kalman filter technique. However, the iterative analyses rely highly on the past data, and can not detect the abrupt change of system parameters. To overcome this drawback, adaptive Kalman filter are proposed using adaptive tracking techniques.
In this thesis, an adaptive tracking technique based on the Kalman filter will be proposed. This proposed method is focus on the development of error covariance matrix. Firstly, the residual error of each time step is calculated, and then the adaptation matrix is generated in accordance with the residual error. Through the proposed error index the on-line adaptive tracking of system parameter can be identified. The proposed method is capable of tracking the abrupt change of parameters from a severe dynamic event. Moreover, it is also applied to identity the backbone curve of the inelastic restoring force of the nonlinear system. To verify the adaptive tracking technique, the responses of the structural system will be simulated numerically and measured experimentally, then, used to identify the parameters in structural system. Finally, the identification results are compared and discussed.
Subjects
卡氏過濾理論
結構健康度檢測
系統識別
傷害檢測
即時識別
Kalman filter
structural health monitoring
system identification
damage detection
on-line identification
Type
thesis
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