Using singular value decomposition to compare correlated modal vectors
Using singular value decomposition to compare correlated modal vectors
复制标题
使用奇异值分解来比较相关模态向量
DOI：
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
J. D. Clerck
中科院分区：
文献类型：
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作者：
J. D. Clerck
需要一种工具来跟踪迭代或重复（可变性）测试中的几种振型估计。模态保证准则 (MAC) 是一种众所周知的模态向量比较工具，但它一次只能比较两个模态向量。提出了一种比较模态矢量多个样本的方法。该方法从振型相关性和对应性开始。建议使用伪正交性来建立对应关系并促进相关模态振型的映射。探索了向量平均的均值向量和奇异值分解（SVD）方法。 SVD 方法产生单个数字向量空间一致性度量。使用简单的集总质量整车模型来评估算法并研究车辆振动对轮胎、悬架、发动机支架和固有车身结构刚度变化的敏感性。
A tool is needed to track several mode shape estimates from iterative or repeated (variability) testing. Modal Assurance Criterion (MAC) is a well known modal vector comparison tool, however it can compare only two modal vectors at a time. A method to compare multiple samples of modal vector is presented The approach begins with mode shape correlation and correspondence. Pseudo orthogonality is recommended to establish correspondence and facilitate mapping of the correlated mode shapes. The mean vector and singular value decomposition (SVD) approaches to vector averaging are explored. The SVD approach yields a single number Vector Space Consistency measure. A simple lumped-mass full vehicle model is used to evaluate the algorithm and study the vehicle vibration sensitivity to variations in tire, suspension, engine mount and inherent body structure stiffness.