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Spurious mode distinguish by eigensystem realization algorithm with improved stabilization diagram

机译:通过本征系统实现算法和改进的稳定图来识别杂散模式

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摘要

Modal parameter identification plays a key role in the structural health monitoring (SHM) for civil engineering. Eigensystem realization algorithm (ERA) is one of the most popular identification methods. However, the complex environment around civil structures can introduce the noises into the measurement from SHM system. The spurious modes would be generated due to the noises during ERA process, which are usually ignored and be recognized as physical modes. This paper proposes an improved stabilization diagram method in ERA to distinguish the spurious modes. First, it is proved that the ERA can be performed by any two Hankel matrices with one time step shift. The effect of noises on the eigenvalues of structure is illustrated when the choice of two Hankel matrices with one time step shift is different. Then, a moving data diagram is proposed to combine the traditional stabilization diagram to form the improved stabilization diagram method. The moving data diagram shows the mode variation along the different choice of Hankel matrices, which indicates whether the mode is spurious or not. The traditional stabilization diagram helps to determine the concerned truncated order before moving data diagram is implemented. Finally, the proposed method is proved through a numerical example. The results show that the proposed method can distinguish the spurious modes.
机译:模态参数识别在土木工程的结构健康监测(SHM)中起着关键作用。本征系统实现算法(ERA)是最流行的识别方法之一。但是,土木结构周围的复杂环境会将噪声引入到SHM系统的测量中。杂散模式将由于ERA过程中的噪声而生成,通常会被忽略并被识别为物理模式。本文提出了一种改进的ERA稳定图方法,以区分杂散模式。首先,证明了ERA可以由任何两个Hankel矩阵执行一次时移。当两个具有一次时移的汉克矩阵的选择不同时,说明了噪声对结构特征值的影响。然后,提出了一种运动数据图,结合了传统的稳定图,形成了改进的稳定图方法。移动数据图显示了随着汉克矩阵的不同选择而产生的模式变化,这表明模式是否为伪模式。传统的稳定图有助于在实施移动数据图之前确定相关的截断顺序。最后,通过数值算例验证了该方法的有效性。结果表明,该方法可以区分杂散模式。

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