Estimating Modal Parameters from Different Solution Sets
Estimating Modal Parameters from Different Solution Sets
复制标题
从不同的解集估计模态参数
DOI：
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
Charles R. Pickrel
中科院分区：
文献类型：
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作者：
A. Phillips;R. Allemang;Charles R. Pickrel
提出了一种模态参数（频率、阻尼和模态矢量）估计方法，该方法利用从不同模型阶数、数据子集和参数估计方法导出的许多估计值。利用 s 平面中模态频率的聚类来识别有效模态频率，而不管模态频率估计的来源如何。当模态向量估计的空间维度一致时，利用模态向量集和/或状态向量集的奇异值分解（SVD）。显示测试数据案例的结果。聚类方法将稳定性和一致性的概念扩展到 s 平面中的模态频率密度。这些方法的一个重要特征是不需要提前知道系统的确切模型阶。模态参数估计是使用模型阶数的变化或估计模态频率的数量在包含和/或超过被测系统的范围内进行的。极簇的数量指示了系统极的数量。簇的大小和位置提供了指示极点的均值和方差的估计。显着奇异值的数量还提供了对 s 平面中极点估计簇中实际模态频率数量的估计。
A method for the estimation of modal parameters (frequency, damping and modal vectors) is presented which utilizes many estimates derived from different model orders, data subsets and parameter estimation methods. Clustering of the modal frequencies in the s-plane is utilized in order to identify valid modal frequencies regardless of the origin of the modal frequency estimate. Singular value decomposition (SVD) of the modal vector sets and/or state vector sets is utilized when the spatial dimension of the modal vector estimates is consistent. Results are shown for test data cases. The clustering method extends the concepts of stability and consistency to what is referred to as a modal frequency density in the s-plane. An important feature of these methods is that the exact model order of the system need not be known in advance. Modal parameter estimates are made using variations in model order, or number of estimated modal frequencies, over a range which encompasses and/or exceeds the system under test. The number of pole clusters provides an indication of the number of system poles. The size and location of the cluster provides an estimate of the mean and variance of the indicated pole. The number of significant singular values also provides an estimate of the number of realistic modal frequencies in the cluster of pole estimates in the s-plane.