Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies wh...Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies which indicate volumetric flowrate from noisy data, especially at low flowrates. Hilbert-Huang transform was adopted to estimate vortex frequency. The noisy raw signal was decomposed into different intrinsic modes by empirical mode decomposition, the time-frequency characteristics of each mode were analyzed, and the vortex frequency was obtained by calculating partial mode’s instantaneous frequency. Experimental results show that the proposed method can estimate the vortex frequency with less than 2% relative error; and in the low flowrate range studied, the denoising ability of Hilbert-Huang transform is markedly better than Fourier based algorithms. These findings reveal that this method is accurate for vortex signal processing and at the same time has strong anti-disturbance ability.展开更多
基金Project(20030335058) supported by the Special Research Fund for the Doctoral Programof Higher Education of China
文摘Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies which indicate volumetric flowrate from noisy data, especially at low flowrates. Hilbert-Huang transform was adopted to estimate vortex frequency. The noisy raw signal was decomposed into different intrinsic modes by empirical mode decomposition, the time-frequency characteristics of each mode were analyzed, and the vortex frequency was obtained by calculating partial mode’s instantaneous frequency. Experimental results show that the proposed method can estimate the vortex frequency with less than 2% relative error; and in the low flowrate range studied, the denoising ability of Hilbert-Huang transform is markedly better than Fourier based algorithms. These findings reveal that this method is accurate for vortex signal processing and at the same time has strong anti-disturbance ability.
