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基于独立分量分析的欠定盲源分离方法
引用本文:杨杰,郑海起,田昊,关珍贞,王彦刚.基于独立分量分析的欠定盲源分离方法[J].振动与冲击,2013,32(7):30-33.
作者姓名:杨杰  郑海起  田昊  关珍贞  王彦刚
作者单位:石家庄军械工程学院 石家庄,050003
摘    要:目前的欠定盲分离算法只能分离稀疏信号,对于不稀疏的信号分离效果不理想。经典独立分量分析算法中的扩展Infomax算法既能分离超高斯信号,也能分离亚高斯信号,但却只能应用于观测数不少于源数的超定盲源分离,结合扩展Infomax算法,本文提出了一种欠定ICA算法,通过生成隐藏数据将欠定盲分离问题转化为超定盲分离问题,然后再应用经典的扩展Infomax算法进行分析,该方法可以分离欠定情形下超高斯和亚高斯混合信号。并用该算法对实测的齿轮箱混合故障信号进行分离,再用包络阶次方法对分离出的信号进行分析,成功识别出了齿轮箱的不同故障特征,验证了该算法在齿轮箱故障诊断中的有效性。

关 键 词:独立分量分析    扩展Infomax    欠定盲源分离:故障诊断  
收稿时间:2011-4-8
修稿时间:2012-4-18

Underdetermined Blind Source Separation Method Based on Independent Component Analysis
YANG Jie,ZHENG Hai-qi,TIAN Hao,GUAN Zhen-zhen,WANG Yan-gang.Underdetermined Blind Source Separation Method Based on Independent Component Analysis[J].Journal of Vibration and Shock,2013,32(7):30-33.
Authors:YANG Jie  ZHENG Hai-qi  TIAN Hao  GUAN Zhen-zhen  WANG Yan-gang
Affiliation:Ordnance Engineering College, Shijiazhuang 050003, China
Abstract:The underdetermined blind source separation(UBSS) algorithm at present can separate sparse signals well, but can’t separate non-sparse signals successfully. Classical ICA algorithms such as extended Infomax can separate both super-Gaussian and sub-Gaussian signals, but it is only used in the over-determined BSS(OBSS). Combining extended Infomax, an underdetermined ICA algorithm is proposed in this text. By generating hidden data, the UBSS problem is transformed into OBSS problem, and then extended Informax algorithm is used to analyze the signal. This method can separated both super-Gaussian and sub-Gaussian signals in the UBSS problem. Through the analysis of transient signal on gearbox by use of underdetermined ICA combined with order envelope spectrum analysis, the fault features are fully detected and its effectiveness is verified.
Keywords:Independent component analysis                                                      Extended Infomax                                                      Underdetermined BSS                                                      Fault Diagnosis
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