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Shift data to distinguish spurious modes in eigensystem realization algorithm

机译:转移数据以区分杂散模式在Eigensystem实现算法中

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Modal parameters, such as frequency, damping ratio and modal shape, play a key role to reflect the structural behaviour. Eigensystem realization algorithm (ERA) has been effectively utilized to many applications to identify the modal parameters. However, the noises in practical environment can influent the effectiveness of ERA, which can introduce spurious modes. This paper proposes a modified ERA to distinguish the spurious modes. It is first proved that any two Hankel matrices with one time step shift used in ERA can obtain the state matrix. The singular values in the singular value matrix obtained by singular value decomposition of Hankel matrix are populated by noises. The truncated order for the singular value matrix cannot be determined. It is proposed that singular value changing can be observed through moving data, which help to identify the spurious mode. The stabilization diagram is employed to obtain the interested modes. Then the proposed moving data diagram can demonstrate the singular value changing which is spurious. Finally, the performance of the proposed method is verified through a numerical example.
机译:模态参数,如频率,阻尼比和模态形状,起到反映结构行为的关键作用。 Eigensystem实现算法(ERA)已有效地利用对许多应用来标识模态参数。然而,实际环境中的噪音可以影响时代的有效性,这可以引入杂散模式。本文提出了一种改进的时代,以区分杂散模式。首先证明,任何两个Hankel矩阵具有在时代使用的一次步骤偏移可以获得状态矩阵。通过兔子基质的奇异值分解获得的奇异值矩阵中的奇异值被噪声填充。无法确定奇异值矩阵的截断顺序。建议通过移动数据可以观察到奇异值改变,这有助于识别杂散模式。使用稳定图来获得感兴趣的模式。然后,所提出的移动数据图可以展示杂散的奇异值变化。最后,通过数值示例验证所提出的方法的性能。

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