Illusive component identification method in noisy signal EMD processing
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摘要: 针对含噪信号Hilbert-Huang变换存在虚假分量,提出改进的奇异值分解(SVD)方法进行降噪,改进包含两个部分:一是利用重构相空间代替传统矩阵如Hankel矩阵,以去掉信号冗余,再者提出奇异值能量熵分量差分法,更易于定出重构奇异值阶次;二是提出了频谱比值法对虚假分量进行辨识,更有效辨识出虚假分量.首先利用经验模式分解(EMD)得到本征模式分量(IMF),识别并剔除趋势项,重构信号,然后进行SVD,重构降噪后的信号,消除虚假分量,最后进行时频分析.联合方法应用于含噪仿真信号,信噪比(signal noise ratio,SNR)提高了5.5%,虚假分量辨识率提高至100%,用于双跨转子故障振动信号,得到正确的时频结果,表明了所提方法识别含噪信号虚假分量的有效性.
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关键词:
- Hilbert-Huang变换 /
- 频谱比值 /
- 虚假分量 /
- 相空间重构 /
- 奇异值分解
Abstract: According to the fact that there exists illusive components when noisy signal is under Hilbert-Huang transform, an improved singular value decomposition (SVD) was firstly proposed for two improvements: one was that the reconstructed phase space was employed instead of traditional matrix such as Hankle matrix in order to remove redundancy, the other was that a singular value energy entropy component difference method was proposed, making it easier to determine order of reconstruction singular value. Secondly, a spectrum ratio method was raised to identify illusive components, making it easier to identify illusive components. Intrisinc mode function (IMF) was obtained by empirical mode decomposition (EMD) and trend term was detrended, then the signal was reconstructed for subsequent SVD operation; the denoised signal was obtained and illusive component was eliminated, lastly the signal time-frequency distribution was calculated. The SNR raised 5.5% when the jointly proposed method employed in simulation signal, the illusive component identity ratio raised to 100%, two-span rotor fault vibration signal analysis results show that the jointly proposed method is effective in time-frequency distribution calculation and identification of noisy signal illusive components. -
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