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。
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.
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
{{custom_keyword}} /
Key words
:Bridge structure /
Mode decomposition algorithm /
End effect /
Clustering analysis /
Signal reconstruction
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1]Huang N E, ShenZ, Long S R, etal. The empiricalmode decomposition and the Hilbert spectrum fornonlinear and non-stationary time series analysis [J].Proceeding of the Royal Society London A, 1998, 454:903-995.
[2]张佳文. 基于振动特性的结构损伤识别方法研究[D]. 长沙理工大学 2009.
Zhang Jia-wen. Research on Structural Damage Identification Method Based On Vibration Performance [D]. Changsha university of science and technology, 2009.
[3]Wu Zhaohua,Huang N E.Ensemble empirical modedecomposition:a noise assisted data analysis method. Advances in Adaptive Data Analysis . 2009.
[4]何星,王宏力,姜伟,王林.改进的自适应EEMD 方法及其应用[J].系统仿真学报.2014(4).
He Xing, Wang Hongli, JiangWei,Wang Li. Improved Adaptive EEMD Method and Its Application [J]. Journal of System Simulation. 2014 (4).
[5]时世晨,单佩韦.基于EEMD的信号处理方法分析和实现[J].现代电子技术. 2011(01).
ShicengShi,Dan peiwei.Analysis and implementation of signal processing method based on EEMD[J].Modern electronic technology. 2011 (01).
[6]Sekhar S C, Sreenivas T V. Novel approach to AM-FM decomposition with applications to speech and music analysis[C] International Conference on Acoustics. 2004.
[7]Wu F, Qu L. An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery[J]. Journal of Sound & Vibration, 2008, 314(3):586-602.
[8]Deng Y. Boundary-processing-techni- que in EMD method and Hil-bert transform[J]. Chinese Science Bulletin, 2001, 46(11):954-960.
[9]张郁山,梁建文,胡聿贤.应用自回归模型处理EMD方法中的边界问题[J]. 自然科学进展,2003,10:48-53.
Zhang Yu-Shan, Liang Jianwen, Hu Yuxian. Using Autoregressive Model Processing the Boundary Problem in the EMD Method[J]. Progress in Natural Science, 2003, 10:48-53.
Yushan zhang, Jianwen Liang, Yuxian Hu. Using autoregressive model in dealing with the boundary problem in the EMD method[J]. Progress in Natural Science, 2003, 10:48-53.
[10]Cheng J, Yu D, Yang Y. Application of support vector regression machines to the processing of end effects of Hilbert–Huang transform[J]. Mechanical Systems & Signal Processing, 2007, 21(3):1197-1211.
[11]唐东明. 聚类分析及其应用研究[D].电子科技大学,2010.
Dongming Tang. Cluster analysis and its application [D]. University of Electronic Science and Technology of China, 2010.
[12]张超,陈建军. 基于EEMD能量熵和支持向量机的齿轮故障诊断方法[J].中南大学学报(自然科学版). 2012(03).
Chao Zhang,Chen Jianjun.Gear Fault Diagnosis Method Based on EMD Energy Entropy and SVM-based[J]. Central South University (Natural Science Edition) 2012 (03).
[13]孙伟峰. 经验模态分解频率分辨率的一种改进方法[J].计算机工程与应用. 2010(01).
Weifeng Sun.Empirical Mode Decomposition frequency resolution of an improved method [J]. Computer Engineering and Applications 2010 (01).
[14]许顺国. 模糊数学综合评判法在水质评价中的应用—以成都市府河为例[J]. 唐山师范学院学报,2007,02:68-70.
ShunguoXu.Application of Fuzzy Comprehensive Evaluation Method in Water Quality Evaluation of Chengdu Fuhe River [J].Journal of Tangshan Normal University, 2007(02):68-70.
[15]徐士代.环境激励下工程结构模态参数识别[D].东南大学,2006.
ShidaiXu.Environmental excitation engineering structures modal parameter identification [D]. Southeast University, 2006.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(2038 KB)
