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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.
Aiming at the existence of false components in Hilbert-Huang transform with noisy signals, an improved SVD method is proposed to reduce noise. The improvement consists of two parts: one is to use the reconstructed phase space to replace the traditional matrix such as Hankel matrix to remove signal redundancy Furthermore, we propose a singular value energy entropy component difference method, which makes it easier to determine the reconstructed singular value order. Second, we propose a spectral ratio method to identify the false component and identify the false component more effectively.First, we use empirical mode decomposition EMD) to get the eigenmodel component (IMF), identifying and removing the trend term, reconstructing the signal, then performing SVD, reconstructing the noise-reduced signal, removing the false component, and finally performing the time-frequency analysis. Signal, SNR (signal noise ratio, SNR) increased by 5.5%, the false component identification rate increased to 100%, for double-rotor fault vibration signal, the correct time-frequency results obtained, indicating that the proposed method to identify noise Validity of signal false components.