北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (2): 395-402.doi: 10.13700/j.bh.1001-5965.2016.0125

• 论文 • 上一篇    下一篇

基于EEMD-Hilbert谱的涡街流量计尾迹振荡特性

姚凤艳1, 周天1,2, 孙志强1   

  1. 1. 中南大学 能源科学与工程学院, 长沙 410083;
    2. 中南大学 材料科学与工程学院, 长沙 410083
  • 收稿日期:2016-02-06 出版日期:2017-02-20 发布日期:2016-09-06
  • 通讯作者: 孙志强,E-mail:zqsun@csu.edu.cn E-mail:zqsun@csu.edu.cn
  • 作者简介:姚凤艳,女,硕士研究生。主要研究方向:多相流测试技术;周天,男,讲师。主要研究方向:热物性测试、多相流测试技术;孙志强,男,博士,教授。主要研究方向:多相流测试技术、新能源与节能技术。
  • 基金资助:
    国家自然科学基金(51576213);中南大学中央高校基本科研业务费专项资金(2015zzts203)

Vortex flowmeter wake fluctuation characteristics based on EEMD-Hilbert spectrum

YAO Fengyan1, ZHOU Tian1,2, SUN Zhiqiang1   

  1. 1. School of Energy Science and Engineering, Central South University, Changsha 410083, China;
    2. School of Materials Science and Engineering, Central South University, Changsha 410083, China
  • Received:2016-02-06 Online:2017-02-20 Published:2016-09-06
  • Supported by:
    National Natural Science Foundation of China (51576213); Fundamental Research Funds for the Central Universities of Central South University (2015zzts203)

摘要: 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 m3/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。

关键词: 涡街流量计, 尾迹振荡, 集总经验模态分解(EEMD), Hilbert变换, 去噪

Abstract: In order to study the wake oscillation characteristics of vortex flowmeter, the ensemble empirical mode decomposition (EEMD)-Hilbert spectral method was employed to analyze the wall differential pressure signal of vortex flowmeter using air as the medium, whose volumetric flow rate is in the range 10.58-220 m3/h. First, the wall pressure differential signal was decomposed by the EEMD method, obtaining the intrinsic mode functions (IMFs), which were later transformed by the Hilbert transform to acquire the Hilbert spectrum and the marginal spectrum. Thus the vortex shedding frequency of the wall pressure differential signals were extracted. The performance of signal denoising and frequency extraction was compared between the Fourier transform and EEMD-Hilbert spectral method. The results show that the EEMD-Hilbert spectral method is fully adequate in eliminating the noise imposed on the vortex signals, resulting in the intact wake oscillation components. In lower flow rate ranges, the EEMD-Hilbert spectral method performs 30% more accurately in extracting the wake frequency compared to the results of the Fourier transform, thus extending the lower effective range of the vortex flowmeter. By computing the energy ratio, the reason for the high accuracy of the EEMD-Hilbert spectral method was uncovered, that is, the EEMD-Hilbert spectral method decreased the signal to noise ratio. The Hilbert spectrum visually depicts the relationship among time, frequency and energy.

Key words: vortex flowmeter, wake fluctuation, ensemble empirical mode decomposition (EEMD), Hilbert transform, denoising

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