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Uncertainty quantification for the Modal Phase Collinearity of complex mode shapes

机译：复杂模式形状的模态相结合性的不确定度量化

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摘要

The Modal Phase Collinearity (MPC) is a modal indicator designed to decide whether the mode shape used in its computation is a real or complex-valued vector. Its estimate inherits the statistical properties of the corresponding mode shape estimate. While the statistical framework for the uncertainty quantification of modal parameters is well-known and developed in the context of subspace-based system identification methods, uncertainty quantification for the MPC estimate has not been carried out yet. In this paper, the uncertainty quantification of the MPC estimates is developed when the corresponding mode shapes are complex-valued vectors. In this case, the theoretical value of the MPC is strictly lower than 1 and it is shown that the distribution of the MPC estimate can be approximated as Gaussian. The computation of its variance and the resulting confidence intervals of the MPC estimate are developed. The proposed framework is validated in Monte Carlo simulations and illustrated on experimental data of an offshore structure.
机译：模态共线相位（MPC）是被设计为决定在其计算中使用的模式形状是否是实数或复数值的矢量模态指示器。其估计继承了对应模式形状估计的统计特性。虽然模态参数的不确定性量化的统计框架是众所周知的，在基于子空间系统辨识方法的背景下开发的，为货币政策委员会估计不确定性量化还没有尚未执行。在本文中，当相应的模式形状是复数值矢量的MPC估计的不确定性量化显影。在这种情况下，MPC的理论值是大于1严格下，它表明，该MPC估计的分布可以近似为高斯分布。其方差和计算的MPC估计得到的置信区间的开发。所提出的框架在Monte Carlo模拟验证和离岸结构的实验数据示出。

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