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Kalman filter-based subspace identification for operational modal analysis under unmeasured periodic excitation
Mechanical Systems and Signal Processing ( IF 7.9 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.ymssp.2020.106996
Szymon Greś , Michael Döhler , Palle Andersen , Laurent Mevel

Abstract The modes of linear time invariant mechanical systems can be estimated from output-only vibration measurements under ambient excitation conditions with subspace-based system identification methods. In the presence of additional unmeasured periodic excitation, for example due to rotating machinery, the measurements can be described by a state-space model where the periodic input dynamics appear as a subsystem in addition to the structural system of interest. While subspace identification is still consistent in this case, the periodic input may render the modal parameter estimation difficult, and periodic modes often disturb the estimation of close structural modes. The aim of this work is to develop a subspace identification method for the estimation of the structural parameters while rejecting the influence of the periodic input. In the proposed approach, the periodic information is estimated from the data with a non-steady state Kalman filter, and then removed from the original output signal by an orthogonal projection. Consequently, the parameters of the periodic subsystem are rejected from the estimates, and it is shown that the modes of the structural system are consistently estimated. Furthermore, standard data analysis procedures, like the stabilization diagram, are easier to interpret. The proposed method is validated on Monte Carlo simulations and applied to both a laboratory example and a full-scale structure in operation.

中文翻译:

基于卡尔曼滤波器的子空间识别用于未测量周期激励下的操作模态分析

摘要 线性时不变机械系统的模式可以通过基于子空间的系统识别方法在环境激励条件下的仅输出振动测量中进行估计。在存在额外的未测量的周期性激励的情况下,例如由于旋转机械,可以通过状态空间模型来描述测量,其中周期性输入动力学作为一个子系统出现在感兴趣的结构系统之外。虽然在这种情况下子空间识别仍然是一致的,但周期性输入可能会使模态参数估计变得困难,并且周期性模态经常干扰紧密结构模态的估计。这项工作的目的是开发一种子空间识别方法,用于估计结构参数,同时拒绝周期性输入的影响。在所提出的方法中,周期性信息是用非稳态卡尔曼滤波器从数据中估计出来的,然后通过正交投影从原始输出信号中去除。因此,周期性子系统的参数从估计中被拒绝,并且表明结构系统的模式是一致估计的。此外,标准数据分析程序,如稳定性图,更容易解释。所提出的方法在蒙特卡罗模拟上得到验证,并应用于实验室示例和运行中的全尺寸结构。周期性子系统的参数从估计中被拒绝,并且表明结构系统的模式是一致估计的。此外,标准数据分析程序,如稳定性图,更容易解释。所提出的方法在蒙特卡罗模拟上得到验证,并应用于实验室示例和运行中的全尺寸结构。周期性子系统的参数从估计中被拒绝,并且表明结构系统的模式是一致估计的。此外,标准数据分析程序,如稳定性图,更容易解释。所提出的方法在蒙特卡罗模拟上得到验证,并应用于实验室示例和运行中的全尺寸结构。
更新日期:2021-01-01
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