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EMD-based stochastic subspace identification of structures from operational vibration measurements

机译:基于EMD的操作振动测量结果对结构的随机子空间识别

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Vibration-based structural health monitoring usually needs to extract vibration characteristics from operational vibration measurements. The stochastic subspace identification (SSI) algorithm is an advanced technique for performing such an operational modal analysis. A newly developed signal processing technique, called empirical mode decomposition (EMD), is capable of dealing with non-stationary signals. An EMD-based stochastic subspace identification procedure utilizing operational vibration measurements is presented in this paper. The output-only measurements are first decomposed into modal response functions by means of the EMD technique, on the basis of specified intermittency frequencies. The stochastic subspace identification method is then applied to the decomposed signals to yield the modal parameters. A case study of the operational measurements from a real bridge is presented, in order to illustrate the applicability of the proposed technique. It is demonstrated that the stable pole in the stabilization diagrams becomes unique and the vibration characteristics are easily identified for the decomposed signals, bypassing the influence of other modal components and fake frequencies due to unwanted noise.
机译:基于振动的结构健康监测通常需要从运行振动测量中提取振动特征。随机子空间识别(SSI)算法是一种用于执行这种操作模式分析的高级技术。一种新开发的信号处理技术,称为经验模式分解(EMD),能够处理非平稳信号。本文提出了一种基于EMD的利用操作振动测量的随机子空间识别程序。首先,在指定的间歇频率的基础上,通过EMD技术将仅输出的测量值分解为模态响应函数。然后将随机子空间识别方法应用于分解后的信号以产生模态参数。为了说明所提出的技术的适用性,提出了对来自真实桥梁的操作测量的案例研究。可以证明,稳定图中的稳定极点变得唯一,并且易于识别分解信号的振动特性,从而避免了由于有害噪声而导致的其他模态分量和伪频率的影响。

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