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Simultaneous identification of structural time-varying physical parameters and unknown excitations using partial measurements

机译:使用部分测量同时识别结构时变形的物理参数和未知激励

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Structural systems often exhibit time-varying dynamic characteristics during their service life due to severe hazards and/or environmental erosion. Therefore, the identification of time-varying structural systems is important. So far, methods based on wavelet multiresolution (WM) analysis have been proposed for the identification of structural time-varying physical parameters. However, full information on both complete structural responses and external excitations were requested in previous approaches, which greatly restricts their applications in practice. To overcome this limitation, an algorithm is proposed in this paper for simultaneous identification of structural time-varying physical parameters and unknown external excitations using only partially measured structural responses. The proposed algorithm is based on the integration of WM analysis and the Kalman filter with unknown input (KF-UI) approach recently developed by the authors. Firstly, structural timevarying physical parameters are decomposed by WM expansion, transforming the identification task into scale coefficients estimation. Then, the KF-UI approach is used for simultaneous identification of structural state and unknown excitations using partially measured structural responses. Finally, the scale coefficients are estimated by nonlinear least-squares optimization and the original time-varying physical parameters are re-constructed. Numerical simulations and an experimental test are conducted to validate the proposed algorithm.
机译:由于严重的危险和/或环境侵蚀,结构系统通常在使用寿命期间表现出时变动态特性。因此,识别时变结构系统很重要。到目前为止,已经提出了基于小波多分辨率(WM)分析的方法来识别结构时变形的物理参数。但是,在以前的方法中要求提供有关完全结构响应和外部激励的完整信息,这极大地限制了他们的应用在实践中。为了克服这种限制,本文提出了一种算法,用于同时仅使用部分测量的结构响应来同时识别结构时变形和未知的外部激励。所提出的算法基于WM分析的集成,并具有作者最近开发的未知输入(KF-UI)方法的卡尔曼滤波器。首先,结构时变形物理参数通过WM扩展分解,将识别任务转换为比例系数估计。然后,KF-UI方法用于使用部分测量的结构响应同时识别结构状态和未知激发。最后,通过非线性最小二乘优化估计刻度系数,并且重新构建原始的时变物理参数。进行数值模拟和实验测试以验证所提出的算法。

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