Underdetermined blind source separation method based on independent component analysis

被引：0

作者：

Ordnance Engineering College, Shijiazhuang 050003, China ^{[1
]}

不详 ^{[2
]}

机构：

来源：

J Vib Shock | 2013年 / 7卷 / 30-33期

关键词：

Gaussian distribution - Independent component analysis - Spectrum analysis - Fault detection - Failure analysis;

D O I：

暂无

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摘要：

The underdetermined blind source separation (UBSS) algorithm at present can only separate sparse signals, 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 used only in an over-determined BSS (OBSS). Combined with an extended Infomax, an underdetermined ICA algorithm was proposed here. By generating hidden data, an UBSS problem was transformed into an OBSS one, and then an extended Informax algorithm was used to analyze the signals. This method could separate both super-Gaussian and sub-Gaussian signals in an UBSS problem. Through analyzing transient signals of a gearbox by use of the underdetermined ICA combined with the order envelope spectral analysis, its fault features were fully detected and the effectiveness of the proposed method was verified.

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