%0 Journal Article
%T 基于奇异值分解的ERA改进算法及模态定阶<br>Improved Eigensystem Realization Algorithm and Mode Order Determination Based on SVD
%A 朱锐
%A 杭晓晨
%A 姜东
%A 费庆国
%A 靳文冰
%J 振动.测试与诊断
%D 2018
%R 10.16450/j.cnki.issn.1004-6801.2018.18
%X 研究了一种基于奇异值分解的ERA改进算法和模态定阶方法。在奇异值分解基础上，根据选定阶次在动态系统中所占比重，提出一种模态定阶指标——奇异值百分比，将该指标应用在改进后的特征系统算法中。首先，利用脉冲响应信号构造初始Hankel矩阵，对此矩阵进行奇异值分解生成去噪后的信号矩阵；其次，根据Cadzow算法重构Hankel矩阵；最后，利用奇异值指标确定模态阶次。通过仿真算例验证了改进后的特征系统实现算法具有良好的抗噪能力，利用定阶指标能有效确定模态阶次、剔除虚假模态，对于阻尼识别精度更高。应用该方法对某三厢车排气系统进行了模态参数识别，通过与LMS系统识别结果比较验证了方法的准确性。<br>An improved eigensystem realization algorithm and mode order determination based on singular value decomposition (SVD) is presented. A mode order indicator, called singular value percentage (SVP), is developed according to the weight of selected modes. The indicator is applied to the improved algorithm to determine mode orders in modal identification. The proposed approach includes four main steps: (1) constructing the initial Hankel matrix; (2) eliminating the noise via SVD and obtaining de-noise signal matrix; (3) reconstructing the Hankel matrix by using Cadzow’s algorithm; (4) utilizing the proposed SVP indicator to determine the mode order. Several simulation cases are studied and it is proved that the improved ERA has a better noise resistance ability. The new indicator SVP is effective in mode order determination and false modes elimination. The modal damping ratio can be identified successfully as well. This improved method is also applied to the modal parameter identification of a sedan exhaust system. The effectiveness is proved by comparing identified results with data from LMS system, especially in determining the mode order
%K 模态识别
%K 特征系统实现算法
%K 奇异值分解
%K 奇异值百分比
%K 模态定阶&lt
%K br&gt
%K modal identification
%K eigensystem realization algorithm
%K singular value decomposition
%K singular value percentage
%K mode order determination
%U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201801018&flag=1