Wavelet based soft threshold denoising for vortex flowmeter
Yuncheng Du, Huaxing Wang, Hongrui Shi, Hongyan Liu · 2009
The vortex flowmeter is widely used due to its unique advantages, but the acquirement and measurement of vortex flowmeter signal in low velocity have not been resolved yet. Wavelet transform is gaining great popularity for denoising and has been proven to be an effective method to extract signal from noise. Based on multi-resolution analysis (MRA) and vortex flowmeter signal, a modified soft threshold filtering method was proposed to realize denoising for vortex flowmeter signal. Daubechies wavelets (dbN) and single branch reconstruction algorithm (SBRA) were adopted in this experiment. Analysis and simulations show that the modified soft threshold algorithm is powerful to separate the vortex flowmeter signal from noise when the output of vortex flowmeter is in low velocity.