A novel method of Coriolis flowmeter phase difference measurement based on improved correlation analysis method

The shortcomings traditional phase difference extraction algorithms have such as being susceptible to noise interference, requiring full cycle conditions, leads to a decrease in phase difference extraction accuracy. To improve the accuracy of the phase difference extraction, two improved correlation analysis methods are proposed in this paper, one of which is based on the relationship between the extreme value of the cross-correlation function and the time difference, the other of which combines correlation analysis method with Hilbert transform. A simulation and experiment were set up to explore the influence of sequence length and signal-to-noise ratio on the extraction accuracy, as well as the influence of Kalman filtering. An experiment is set to compare the extraction mean error and variance of different algorithms under different phase differences. Experimental result shows that the improved algorithms have higher accuracy and stability. The feasibility of the two improved algorithms is verified.

Automated summary

This study proposes two improved correlation analysis methods to enhance the accuracy of phase difference extraction. Simulation and experiments were conducted to investigate the impact of sequence length, signal-to-noise ratio, and Kalman filtering on extraction accuracy, as well as to compare different algorithms under different phase differences. The experimental results demonstrate that the improved algorithms have higher accuracy and stability, verifying the feasibility of the proposed methods.