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Kalman filter-based subspace identification for operational modal analysis under unmeasured periodic excitation

机译：基于Kalman滤波器的Subspace识别，用于未测量的周期性激励下的操作模态分析

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摘要

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.
机译：线性时间不变机械系统的模式可以根据基于子空间的系统识别方法在环境激励条件下的输出振动测量估计。在存在额外的未测量的周期性激励中，例如由于旋转机器，可以通过诸如感兴趣的结构系统之外的周期性输入动态作为子系统的状态空间模型来描述测量。虽然在这种情况下，虽然子空间识别仍然是一致的，但周期性输入可以使模态参数估计难以困难，并且周期性模式经常干扰近结构模式的估计。这项工作的目的是开发用于估计结构参数的子空间识别方法，同时抑制周期性输入的影响。在所提出的方法中，从具有非稳态卡尔曼滤波器的数据估计周期性信息，然后通过正交投影从原始输出信号移除。因此，从估计拒绝周期性子系统的参数，并且显示了结构系统的模式一致地估计。此外，标准数据分析程序，如稳定图，更容易解释。该方法在蒙特卡罗模拟上验证并应用于实验室示例和操作中的全尺度结构。

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