Abstract

As a time domain system identification method, the extended Kalman filter (EKF) has been continuously used for structural damage identification. The performance of the EKF varies greatly depending on the selection of the initial parameters’ values and their combinations. In this paper, to improve the convergence performance of the EKF and to overcome the dependence on the setting parameter values, a hybrid extended Kalman filter (HEKF) for structural damage identification is proposed. As significant properties of the EKF, the structural damage identification, global convergence, stability, and robustness of the HEKF are guaranteed by integrating a genetic algorithm and the EKF. The performance of the HEKF in structural damage identification was investigated in experiments with four 3-story steel frame test models, which were designed for four different damage scenarios.

摘要

作为一种时域系统识别方法，扩展卡尔曼滤波器（EKF）一直被用于结构损伤识别。EKF 的性能根据初始参数值及其组合的选择而有很大差异。在本文中，为了提高 EKF 的收敛性能并克服对设置参数值的依赖性，提出了一种用于结构损伤识别的混合扩展卡尔曼滤波器（HEKF）。作为 EKF 的重要特性，HEKF 的结构损伤识别、全局收敛、稳定性和鲁棒性是通过集成遗传算法和 EKF 来保证的。HEKF 在结构损伤识别中的性能在四个 3 层钢框架测试模型的实验中进行了研究，