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首页> 外文期刊>Journal of Sound and Vibration >Merging sensor data from multiple measurement set-ups for non-stationary subspace-based modal analysis
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Merging sensor data from multiple measurement set-ups for non-stationary subspace-based modal analysis

机译:合并来自多个测量设置的传感器数据,以进行基于非平稳子空间的模态分析

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摘要

Processing sensor data, from multiple non-simultaneously recorded measurement set-ups, for structural analysis is often achieved by merging identification results obtained from records corresponding to different sensor pools. Since pole matching and eigenvector gluing might not be consistent in some cases, the question arises to merge the data first and then process them globally. Subspace identification algorithms have proven efficient for performing output-only modal analysis of mechanical systems subject to uncontrolled, unmeasured, and non-stationary excitation. The purpose of this paper is to investigate as to how subspace-based output-only modal analysis algorithms can be adapted to handle the multi-patch measurements set-up. Numerical results, obtained on both laboratory and real application examples, are reported., which show the relevance and usefulness of the proposed algorithm. (C) 2002 Academic Press. [References: 31]
机译:通常通过合并从对应于不同传感器池的记录中获得的标识结果,来处理来自多个非同时记录的测量设置中的传感器数据以进行结构分析。由于极点匹配和特征向量粘连在某些情况下可能不一致,因此出现了先合并数据然后进行全局处理的问题。事实证明,子空间识别算法可以有效地对机械系统进行仅输出的模态分析,从而对机械系统进行不受控制,未经测量和不稳定的激励。本文的目的是研究基于子空间的仅输出模态分析算法如何适用于处理多面体测量设置。报告了在实验室和实际应用实例上获得的数值结果,表明了该算法的相关性和实用性。 (C)2002学术出版社。 [参考:31]

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