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Enhanced stabilization diagram for automated modal parameter identification based on power spectral density transmissibility functions

机译：基于功率谱密度传输功能的自动模态参数识别增强稳定图

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摘要

Operational modal analysis based on power spectral density transmissibility (PSDT) functions is a useful tool to identify the modal parameters with low sensitivity to excitations. For pole extraction from the PSDT function, a proper parametric identification method such as the polyreference least squares complex frequency-domain method or poly-Max method can be used. Then, the poles are selected from a stabilization diagram (SD) with overestimating the system model order. Therefore, spurious modes can be identified that must be distinguished and removed from the system poles. To reach this aim, many techniques have been proposed and applied. In this paper, a new algorithm is proposed to enhance the performance of the SD for automated modal parameter identification based on the PSDT. The algorithm is composed of two main phases. In the first phase, the spurious modes are discriminated from the system poles on the basis of the conventional and supplementary stability criteria. On spurious mode omission, two new criteria named pole criterion and coherence criterion are introduced and applied as the supplementary stability criteria to make a more clear SD. Then, the extracted poles are categorized in the distinct clusters through a new strategy for comparing modes. In the second phase, a novel multiscreening algorithm is implemented for the automated identification of the system poles. Accordingly, the searching and averaging processes are followed between clusters, and the poles are screened to automatically identify the system poles on the basis of the numbers of their repetition in the SD via k-means clustering algorithms. Also, to improve the accuracy of the identification, the Hilbert transform is used in the construction of the PSDT functions. Finally, to validate and demonstrate the efficiency of the proposed method, a computer simulation and an experiential case study are considered.
机译：基于功率谱密度传输（PSDT）功能的操作模态分析是一种有用的工具，用于识别具有低灵敏度的敏感性的模态参数。对于来自PSDT功能的极点提取，可以使用适当的参数识别方法，例如聚频率最小二乘复杂频域方法或多个MAX方法。然后，通过高估系统模型顺序，从稳定图（SD）中选择极点。因此，可以识别杂散模式，必须从系统极点区分和移除。为了达到这个目的，已经提出并应用了许多技术。在本文中，提出了一种新的算法来增强基于PSDT的自动模态参数识别的SD的性能。该算法由两个主要阶段组成。在第一阶段，基于传统和补充稳定标准，寄生模式与系统极极区分。在虚假模式遗漏时，引入了两个名为极值标准和一致性标准的新标准，并作为补充稳定标准，以制造更清晰的SD。然后，提取的极通过用于比较模式的新策略在不同的群集中分类。在第二阶段，实现了一种新的多筛选算法，用于系统磁极的自动识别。因此，在集群之间遵循搜索和平均过程，并且屏蔽极点以通过K-Means聚类算法基于其在SD中重复的数量来自动识别系统极点。此外，为了提高识别的准确性，Hilbert变换用于PSDT功能的结构。最后，为了验证和证明所提出的方法的效率，考虑计算机仿真和经验案例研究。

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