CMF Signal Processing Method Based on Feedback Corrected ANF and Hilbert Transformation

Abstract

In this paper, we focus on CMF signal processing and aim to resolve the problems of precision sharp-decline occurrence when using adaptive notch filters (ANFs) for tracking the signal frequency for a long time and phase difference calculation depending on frequency by the sliding Goertzel algorithm (SGA) or the recursive DTFT algorithm with negative frequency contribution. A novel method is proposed based on feedback corrected ANF and Hilbert transformation. We design an index to evaluate whether the ANF loses the signal frequency or not, according to the correlation between the output and input signals. If the signal frequency is lost, the ANF parameters will be adjusted duly. At the same time, singular value decomposition (SVD) algorithm is introduced to reduce noise. And then, phase difference between the two signals is detected through trigonometry and Hilbert transformation. With the frequency and phase difference obtained, time interval of the two signals is calculated. Accordingly, the mass flow rate is derived. Simulation and experimental results show that the proposed method always preserves a constant high precision of frequency tracking and a better performance of phase difference measurement compared with the SGA or the recursive DTFT algorithm with negative frequency contribution.

A Phase Difference Measurement Method Based on the Extended Kalman Filter for Coriolis Mass Flowmeters

“…Both SGA and DTFT algorithms need to know the frequency in advance to calculate the phase difference. The HT method is computationally efficient and can obtain the phase difference without information on signal frequency [14]. However, its anti-interference performance is poor and it needs a strict prefilter.…”

“…However, its anti-interference performance is poor and it needs a strict prefilter. Singular value decomposition (SVD) [14] and polyphase decimation filtering and band-pass filtering [13] have been applied to CMF to filter out noises. A phase difference measurement method based on the extended Kalman filter (EKF) for CMF is proposed in this paper.…”

An improved phase interpolation estimator

“…The measurement precision of CMF is mainly dependent on estimating the phase difference and frequency from the CMF signal. Therefore, accurate phase difference and frequency estimation is very important in CMF field [4][5][6][7].…”

Evaluation of Uncertainty of Phase Difference Determination in Presence of Bias

“…, is given in an analogous way to (14): (16) where: 8 c to 14 c are sensitivity coefficients for each sample k U 2 and standard uncertainties have analogous explanations to those in (14). The sensitivity coefficients are calculated in an analogous way to (15).…”