<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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</p></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

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

Logistical Engineering University, Chongqing 401311, P.R.C

Measurement Science Review

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)...

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

Online veröffentlicht: 06. März 2014

Seitenbereich: 41 - 47

DOI: https://doi.org/10.2478/msr-2014-0007

Schlüsselwörter
Coriolis mass flow meter, adaptive notch filter, Hilbert transform, frequency track, phase difference

This content is open access.

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

Online veröffentlicht: 06. März 2014

Seitenbereich: 41 - 47

DOI: https://doi.org/10.2478/msr-2014-0007

SchlüsselwörterCoriolis mass flow meter, adaptive notch filter, Hilbert transform, frequency track, phase difference

This content is open access.

Schlüsselwörter
Coriolis mass flow meter, adaptive notch filter, Hilbert transform, frequency track, phase difference