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Numerical and experimental analysis of uncertainty on modal parameters estimated with the stochastic subspace method

机译:随机子空间法估算模态参数的数值和实验分析

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摘要

Modal parameters of structures are often used as inputs for finite element model updating, vibration control, structural design or structural health monitoring (SHM). In order to test the robustness of these methods, it is a common practice to introduce uncertainty on the eigenfrequencies and modal damping coefficients under the form of a Gaussian perturbation, while the uncertainty on the mode shapes is modeled in the form of independent Gaussian noise at each measured location. A more rigorous approach consists however in adding uncorrelated noise on the time domain responses at each sensor before proceeding to an operational modal analysis. In this paper, we study in detail the resulting uncertainty when modal analysis is performed using the stochastic subspace identification method. A Monte-Carlo simulation is performed on a simply supported beam, and the uncertainty on a set of 5000 modal parameters identified with the stochastic subspace identification method is discussed. Next, 4000 experimental modal identifications of a small clamped-free steel plate equipped with 8 piezoelectric patches are performed in order to confirm the conclusions drawn in the numerical case study. In particular, the results point out that the uncertainty on eigenfrequencies and modal damping coefficients may exhibit a non-normal distribution, and that there is a non-negligible spatial correlation between the uncertainty on mode shapes at sensors of different locations.
机译:结构的模态参数通常用作有限元模型更新,振动控制,结构设计或结构健康监测(SHM)的输入。为了测试这些方法的稳健性,是一种常见的做法,在高斯扰动的形式下引入特征频繁和模态阻尼系数的常见做法,而模式形状的不确定性是以独立高斯噪声的形式建模的。每个测量的位置。然而,更严格的方法包括在进行操作模态分析之前在每个传感器的时域响应上添加不相关的噪声。在本文中,我们详细研究了使用随机子空间识别方法进行模态分析时产生的不确定性。在简单的支持光束上执行蒙特卡罗模拟,并且讨论了用随机子空间识别方法识别的一组5000模态参数的不确定性。接下来,执行配备有8个压电贴片的小夹紧的钢板的4000个实验模态识别,以确认在数值案例研究中绘制的结论。特别是,结果指出,特征频道和模态阻尼系数的不确定性可以表现出非正态分布,并且在不同位置的传感器处的不确定性之间存在不可忽略的空间相关性。

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