An adaptive extended Kalman filter for structural damage identification

Abstract

The identification of structural damage is an important objective of health monitoring for civil infrastructures. System identification and damage detection based on measured vibration data have received intensive studies recently. Frequently, damage to a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, we propose an adaptive tracking technique, based on the extended Kalman filter approach, to identify the structural parameters and their changes when vibration data involve damage events. The proposed technique is capable of tracking the changes of system parameters from which the event and severity of structural damage may be detected on‐line. Our adaptive filtering technique is based on the current measured data to determine the parametric variation so that the residual error of the estimated parameters is contributed only by noise. This technique is applicable to linear and nonlinear structures. Simulation results for tracking the parametric changes of nonlinear elastic, hysteretic and linear benchmark structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting structural damage, using measured vibration data. Copyright © 2005 John Wiley & Sons, Ltd.

Identification of Structural Systems Using Particle Swarm Optimization

“…Currently, a wide range of analytical methods exist for linear or nonlinear system identification. Most common among these methods are the least squares method (Yang and Lin (2004); Tang et al (2006); Yang et al (2007)), the maximum likelihood method (Campillo and Mevel (2005)), the extended Kalman filter (Yang et al (2005)), the H ∞ filter method (Sato and Qi (1998)), and the particle filter method (Li et al (2004); Tang and Sato (2005)). Most of these methods require an initial guess to start the process.…”

Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

“…Figure 1 describes the estimation error during the simulation process. It is obvious that if the conditions in (18) to (20) are satisfied, then the estimation error remains bounded for both the EKF and the AFEKF. In Figure 2, the sum of the squared estimation errors are shown.…”

“…On the other hand, both the KF and the EKF might give biased estimates and diverge when the initial estimates are not sufficiently good or the arbitrary noise matrices have not been chosen appropriately or any changes occur in the system dynamics [6,7]. To overcome these problems, sevaral adaptive filtering techniques [8][9][10][11][12][13][14][15][16][17][18] are proposed. Among them is the adaptive fading extended Kalman filter with the matrix forgetting factor (AFEKF) [8].…”

Localized Structural Health Monitoring Using Energy-Efficient Wireless Sensor Networks

“…Various other damage detection algorithms have been proposed in SHM, for example, wavelet analysis [18], adaptive extended Kalman filter [19] and Hilbert-Huang transformation [20]. For detailed review of vibration-based damage identification methods, readers are referred to [3] and [7].…”