【摘要】 针对基于希尔波特黄变换(HHT)的时频谱存在受噪声影响大、高频部分时频分辨率低的缺点,提出将迭代奇异值分解(ISVD)降噪和重排谱图应用于HHT时频谱分析中。首先对噪声对HHT时频谱的影响进行了分析,含噪实测信号经过经验模式分解(EMD)得到的基本模式分量(IMF)中含有很强的噪声成分,从中提取的瞬时参数不具有物理意义,构成的HHT时频谱混乱而不准确;然后利用ISVD降噪对实测信号进行了降噪处理,有效地去除了噪声,降噪后信号EMD分解得到的IMF分量其Renyi信息接近于0,是近似平稳的单分量信号,从中可以提取到准确的瞬时参数,构成特征清晰、分布合理的HHT时频谱;最后对HHT时频谱中高频部分所对应的IMF分量进行了重排谱图分析,实现了其时频特征的准确定位。结果表明,该方法可以有效地提高烟气轮机信号HHT时频谱分析的准确率,为故障诊断提供可靠的特征依据。更多还原

【Abstract】 In order to improve the quality of time-frequency spectrum based on Hilbert-Huang transform(HHT),the iterative singular value decomposition(ISVD) de-noising and reassigned spectrogram are applied in this paper.Firstly,the influence of noise is discussed.Using empirical mode decomposition(EMD),the intrinsic mode function(IMF) obtained from the noise-contained signal contains a lot of noise.So the instantaneous parameters obtained from IMF are not correct and the HHT time-frequency spectrum has no physical meaning.Then the iterative singular value decomposition(ISVD) de-noising is applied.Through ISVD de-noising,noise is reduced effectively and the Renyi information of the IMF is near zero.As the IMF becomes single component signal,the obtained instantaneous parameters are accurate and the HHT time-frequency spectrum is reasonable.Finally,because the HHT time-frequency spectrum has low time-frequency resolution in high-frequency domain,the IMF relevant to this domain is analyzed using reassigned spectrogram.The time-frequency feature is extracted accurately.Results prove that the method proposed in this paper is effective and can improve the accurate rate of fault diagnosis of flue gas turbine.更多还原