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首页> 外文期刊>Journal of Sound and Vibration >Identification of modal parameters from measured output data using vector backward autoregressive model
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Identification of modal parameters from measured output data using vector backward autoregressive model

机译:使用向量反向自回归模型从测量的输出数据中识别模态参数

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In this paper. a modal identification system that is based on the vector backward autoregressive (VBAR) model has been developed for the identification of natural frequencies. damping ratios and mode shapes of structures from measured output data. The modal identification using forward autoregressive approach has some problems in discriminating the structure modes from spurious modes. On the contrary, the VBAR approach provides a determinate boundary for the separation of system modes from spurious modes, and an eigenvalue filter for the selection of physical modes is existent in the proposed method. For convenience of application, the backward state equation established from VBAR model is transformed into a forward state equation, which is termed as transformed VFAR model in this paper. In addition, the extraction of equivalent system matrix of state equation of motion for structures from the transformed VFAR model has been developed, and then the normal modes can be calculated from the identified equivalent system matrix, Two examples of modal identification are carried out to demonstrate the availability and effectiveness of the proposed backward approach: (1) Numerical modal identification for a three-degree-of-freedom dynamic system with noise level in 20% of r.m.s of measured output data: (2) experimental modal identification of a cantilever beam. Finally, to show the advantage of the proposed VBAR approach on the selection of physical modes. the modal identification by stochastic subspace method was performed. The results from both methods are compared. (C) 2002 Elsevier Science Ltd. All rights reserved. [References: 29]
机译:在本文中。已经开发了一种基于矢量后向自回归(VBAR)模型的模态识别系统,用于识别固有频率。测得的输出数据得出的结构的阻尼比和模式形状。使用正向自回归方法进行模态识别在区分结构模式和伪模式方面存在一些问题。相反,VBAR方法为系统模式与杂散模式的分离提供了确定的边界,并且该方法中存在用于选择物理模式的特征值滤波器。为方便应用,将VBAR模型建立的后向状态方程式转换为前向状态方程式,本文称之为转换后的VFAR模型。此外,还开发了从转换后的VFAR模型中提取结构运动状态方程的等效系统矩阵,然后可以从识别出的等效系统矩阵中计算出正常模式,并通过两个模态识别示例进行了演示。所提出的后向方法的有效性和有效性:(1)具有20%的测量输出数据均方根值的噪声水平的三自由度动态系统的数值模态识别:(2)悬臂梁的实验模态识别。最后,以展示所提出的VBAR方法在物理模式选择上的优势。通过随机子空间方法进行模态识别。比较两种方法的结果。 (C)2002 Elsevier ScienceLtd。保留所有权利。 [参考:29]

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