基于FastICA-EEMD的振动信号特征提取  

Extraction of vibration signal features based on FastICA-EEMD

作　　者：赵佳佳[1] 贾嵘[1] 武桦[1] 董开松 党建[1] 

机构地区：[1]西安理工大学水利水电学院,西安710048 [2]甘肃省电力科学研究院,兰州730050

出　　处：《水力发电学报》2017年第3期63-70,共8页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金(51279161);陕西省水利科技计划项目(2015slkj-04);电网公司科技项目(522722150012)

摘　　要：针对水轮发电机组的振动信号之间相互影响且容易受到噪声干扰的问题,提出了一种基于快速独立分量分析(FastICA)和集合经验模态分解(EEMD)的故障特征提取方法。首先,利用快速独立分量分析将原始信号分离成若干个独立分量;然后对每个分量均进行集合经验模态分解,根据归一化能量与归一化相关系数两个参数来选取有效的本征模态分量(IMF);最后将其进行重构以获得对应的故障特征。通过仿真与实例分析,并与其他方法进行对比,结果表明该方法可以有效抑制噪声干扰,更为全面、准确地提取到水轮发电机组的振动特征信号,满足实际工程需求。This paper describes a method for extracting fault features from vibration signals based on fast independent component analysis（FastICA） and ensemble empirical mode decomposition（EEMD） to overcome the problems of mutual influence between vibration signals of a hydro-generator unit and noise disturbance to them. First, a vibration signal is decomposed into independent components by FastICA; then, each component is processed by EEMD and its effective intrinsic mode functions（IMFs） are selected by calculating the normalized energy and normalized correlation coefficients of all the IMFs; finally, these effective IMFs are used to extract its fault features. The results of simulation and application show that in comparison with other methods, this method can effectively suppress noise interference and it is more comprehensive and accurate in extraction of vibration feature signals for hydro-generator units, fully meeting practical engineering demands.

关 键 词：水轮发电机组 振动信号 快速独立分量分析 集合经验模态分解 本征模态分量 特征提取 

分 类 号：TM312[电气工程—电机]