A new Developed Modal Parameter Identification Method Based on Empirical Mode Decomposition and Natural Excitation Technique

被引:6
|
作者
Song, Xingyu [1 ]
Ma, Hongwei [1 ]
Wang, Kun [1 ]
机构
[1] Yang Zhou Univ, Coll Civil Sci & Engn, Yangzhou 225000, Jiangsu, Peoples R China
关键词
Natural Excitation Technique; Cross-Correlation Function; Empirical Mode Decomposition; frequency; damping ratio;
D O I
10.1016/j.proeng.2017.09.270
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Natural excitation technique(NExT) is a kind of frequently used method. Traditional natural excitation technique uses cross-correlation function that responses between two points to replace impulse response function, identifying parameters with time domain modal identification method. Its shortcomings lie in that the cross-correlation function got through this method reflects influences of each mode, thus significant errors are seen in calculation results and even false modes are generated. For this reason, this article combines empirical mode decomposition with intermittency check criterion with natural excitation technique and puts forward the natural excitation technique based on empirical mode decomposition. To do this, first, the tested dynamic response signals, v1 and v2 of the structural two points are to decomposed by EMD, in which the mixed modes are eliminated with empirical mode decomposition with cut-off frequency and Intrinsic Mode Functions IMF_v(1)(i) and IMF_v(2)(i) can be tested respectively. Intrinsic Mode Functions IMF_v(1)(i) and IMF_v(2)(i) of the two points that are corresponding to the same modal response are used as modal identification signals and NExT is used to gain cross-correlation function between the two points of this mode. Then the cross-correlation function is taken as the initial input signal to identify parameters with modal identification method in time domain. Numerical simulation and engineering examples show that the proposed method could accurately identify the modal frequency and modal damping ratio of the structure. (c) 2017 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:1020 / 1025
页数:6
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