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Structural Dynamic Load and Parameter Identification Based on Dummy Measurements of Displacement

机译:基于伪测量的位移的结构动态负荷和参数识别

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Structural load and parameter identification are the essential contents in the field of structural dynamics, and some studies pay attention to the coupled recognition of uncertain structure parameters as well as unacquainted loads. Gillijns and De Moor presented the Kalman-type filter, which was used in the work for coupled identification as the unabbreviated form of GDF. However, it has been demonstrated that it is unstable for the extended GDF (EGDF) method, drifting in the identified unacquainted loads as well as displacement, just like most previous identification methods based on the least-square algorithm. In order to deal with this unstable issue, this paper applied the dummy measurements of displacements on a position level and modifies the EGDF algorithm using the information integration method about the accelerated measurements with dummy measurements. Numerical example of a truss is used for validating the applicability of the method in the work, and an influence of covariance matrices in dummy displacements is also considered.
机译:结构负荷和参数识别是结构动态领域的基本内容,有些研究注意耦合对不确定结构参数以及不知情的载荷。 Gillijns和De Moor介绍了卡尔曼型过滤器,该滤波器用于耦合识别作为GDF的未构造形式的工作中。然而,已经证明,扩展GDF(EGDF)方法是不稳定的,在所识别的不知情负载中漂移以及位移,就像基于最小二乘算法的最先前的识别方法一样。为了处理这种不稳定的问题,本文将位移对位置级别的伪测量应用于使用钝化测量的加速测量的信息集成方法修改EGDF算法。桁架的数值例用于验证工作中的方法的适用性,并且还考虑了协方差矩阵在虚拟位移中的影响。

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