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为了研究涡街流量计尾迹振荡特征,采用集总经验模态分解(EEMD)-Hilbert谱方法,对测量介质为空气、流量范围为10.58~220 m~3/h的涡街流量计管壁差压信号进行处理,首先用EEMD方法对管壁差压信号进行分解,得到固有模态分量,然后对分解后的各个分量进行Hilbert变换,得到Hilbert谱和边际谱,进而提取管壁差压信号的旋涡脱落频率。比较了Fourier变换与EEMD-Hilbert谱方法在信号去噪和频率提取方面的性能。结果表明:EEMD-Hilbert谱方法可有效去除叠加在实际涡街成分之中的噪声,能够较完整保留尾迹振荡的固有成分;在流量较低时,EEMD-Hilbert谱方法对尾迹振荡频率的提取精度比Fourier变换高30%以上,有效拓展了涡街流量计的测量下限;通过计算能量比,揭示了EEMD-Hilbert谱方法提高频率提取精度的原因,即EEMD-Hilbert谱方法降低了信噪比;Hilbert谱直观表示信号的时间-频率-能量关系。
In order to study the wake oscillation characteristics of the vortex flowmeter, the vortex flowmeter with a measuring range of 10.58 ~ 220 m ~ 3 / h is used to measure the wake wake characteristics of the vortex flowmeter by the method of lumped empirical mode decomposition (EEMD) -Hilbert spectrum Pressure signal processing, the EEMD method is used to decompose the pipe wall differential pressure signal to obtain the natural modal components, and then the Hilbert transform is performed on the decomposed components to obtain the Hilbert spectrum and the marginal spectrum, then the pipe wall differential pressure signal is extracted Vortex shedding frequency. The performances of Fourier transform and EEMD-Hilbert spectral methods in signal denoising and frequency extraction are compared. The results show that the EEMD-Hilbert spectral method can effectively remove the noise superimposed on the actual vortex components, and can retain the inherent components of the wake wake completely. When the flow rate is low, the EEMD-Hilbert spectral method can improve the precision of wake oscillation frequency extraction Which is more than 30% higher than the Fourier transform, effectively expanding the measurement lower limit of the vortex flowmeter. By calculating the energy ratio, the reason why the EEMD-Hilbert spectral method improves the frequency extraction precision is disclosed, that is, the EEMD-Hilbert spectral method reduces the signal-to- The Hilbert spectrum represents the signal’s time-frequency-energy relationship visually.