基于总体平均经验模态分解算法的自适应改进

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摘要目前较为广泛使用的总体平均经验模态分解算法（EEMD）不能实现桥梁结构响应信号的自适应分解和重构，基于此，本文针对EEMD算法的不足，提出了能够实现桥梁结构响应信号自适应分解与重构的改进算法：首先引入自适应极值点匹配延拓算法以抑制端点效应；再对分解信号进行聚类分析以避免模态混叠现象；最后利用各本征模态函数（IMF）对应的信息熵、能量密度和平均周期构建筛选有效IMF分量的指标（有效程度系数），以实现有效IMF分量的自动筛选，再利用筛选出的有效分量对桥梁结构响应信号进行重构。模拟信号和简支梁桥仿真算例表明，本文所提改进算法能够更有效、更准确的实现桥梁结构响应信号的自适应分解与重构。

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	徐健1
	周志祥1
	唐亮1
	冉杰2
	何杰3

关键词 ：桥梁结构, 模态分解算法, 端点效应, 聚类分析, 信号重构

Abstract：At present, the overall average empirical mode decomposition algorithm (EEMD) is widely used, but it cannot achieve the adaptive decomposition and reconstruction of bridge structure response signal. Based on this, this paper aiming at the shortcomings of the EEMD algorithm, proposed an improved algorithm which can be implemented on adaptive decomposition and reconstruction of bridge structure response signal: firstly, introducing the adaptive extremum points matching with the continuation algorithmto suppress the endpoint effect;secondly, taking Clustering analysis with decomposition signal to avoid modal aliasing phenomenon;finally,using the corresponding intrinsic mode function (IMF) of information entropy, energy density and average period to establish and screening the effective IMF components index(effective degree coefficient), so as to realize the automatic selection of effective IMF components; then having reconstruction for the bridge structure response signal by using the selected effective components. From the Analog signal and simply supported girder bridge simulation examples, it show that this proposed algorithm can achieve adaptive decomposition and reconstruction of bridge structure response signal more effective and accurate.

Key words： :Bridge structure Mode decomposition algorithm End effect Clustering analysis Signal reconstruction

收稿日期: 2016-05-20 出版日期: 2017-05-25

引用本文:

徐健1，周志祥1，唐亮1，冉杰2，何杰3. 基于总体平均经验模态分解算法的自适应改进[J]. 振动与冲击, 2017, 36(11): 215-223.
Jian Xu1,Zhixiang Zhou1,Liang Tang1,Jie Ran2,Jie He3. Adaptive Improvement Algorithm Based on the Overall Average Empirical Mode Decomposition. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(11): 215-223.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2017/V36/I11/215

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