A time-varying signal processing method for Coriolis mass flowmeter based on adaptive filter

Feng Dan; Fan Shangchun; Zheng Dezhi

doi:10.1177/0142331216652955

A time-varying signal processing method for Coriolis mass flowmeter based on adaptive filter

Feng Dan^*, Fan Shangchun, Zheng Dezhi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

In this paper, the normalized least mean square (NLMS) algorithm, a time-varying signal processing method, is employed in a Coriolis mass flowmeter (CFM) to improve its weak anti-jamming capability. Initially, the fundamental principles of the NLMS algorithm adopted in the adaptive filter are analysed. Then, the NLMS algorithm is applied to analyse the signal processing of the CFM at different flow rates in experiments. By comparing several performance indicators and spectrum diagrams from being filtered by the NLMS algorithm and the least mean square (LMS) algorithm, the results indicate that the NLMS algorithm can lead to a better anti-jamming capability and reduce the influence of noise efficiently for the CFM. In addition, the NLMS method has a faster convergence speed and fewer stable errors than the LMS method. Therefore, the NLMS can improve the quality of the output signal of the CFM.

Original language	English
Pages (from-to)	261-268
Number of pages	8
Journal	Transactions of the Institute of Measurement and Control
Volume	40
Issue number	1
DOIs	https://doi.org/10.1177/0142331216652955
Publication status	Published - 1 Jan 2018
Externally published	Yes

Keywords

Adaptive filter
Coriolis mass flowmeter
least mean square
normalized least mean square
signal processing

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10.1177/0142331216652955

Cite this

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title = "A time-varying signal processing method for Coriolis mass flowmeter based on adaptive filter",

abstract = "In this paper, the normalized least mean square (NLMS) algorithm, a time-varying signal processing method, is employed in a Coriolis mass flowmeter (CFM) to improve its weak anti-jamming capability. Initially, the fundamental principles of the NLMS algorithm adopted in the adaptive filter are analysed. Then, the NLMS algorithm is applied to analyse the signal processing of the CFM at different flow rates in experiments. By comparing several performance indicators and spectrum diagrams from being filtered by the NLMS algorithm and the least mean square (LMS) algorithm, the results indicate that the NLMS algorithm can lead to a better anti-jamming capability and reduce the influence of noise efficiently for the CFM. In addition, the NLMS method has a faster convergence speed and fewer stable errors than the LMS method. Therefore, the NLMS can improve the quality of the output signal of the CFM.",

keywords = "Adaptive filter, Coriolis mass flowmeter, least mean square, normalized least mean square, signal processing",

author = "Feng Dan and Fan Shangchun and Zheng Dezhi",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2018.",

year = "2018",

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doi = "10.1177/0142331216652955",

language = "English",

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TY - JOUR

T1 - A time-varying signal processing method for Coriolis mass flowmeter based on adaptive filter

AU - Dan, Feng

AU - Shangchun, Fan

AU - Dezhi, Zheng

N1 - Publisher Copyright: © The Author(s) 2018.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - In this paper, the normalized least mean square (NLMS) algorithm, a time-varying signal processing method, is employed in a Coriolis mass flowmeter (CFM) to improve its weak anti-jamming capability. Initially, the fundamental principles of the NLMS algorithm adopted in the adaptive filter are analysed. Then, the NLMS algorithm is applied to analyse the signal processing of the CFM at different flow rates in experiments. By comparing several performance indicators and spectrum diagrams from being filtered by the NLMS algorithm and the least mean square (LMS) algorithm, the results indicate that the NLMS algorithm can lead to a better anti-jamming capability and reduce the influence of noise efficiently for the CFM. In addition, the NLMS method has a faster convergence speed and fewer stable errors than the LMS method. Therefore, the NLMS can improve the quality of the output signal of the CFM.

AB - In this paper, the normalized least mean square (NLMS) algorithm, a time-varying signal processing method, is employed in a Coriolis mass flowmeter (CFM) to improve its weak anti-jamming capability. Initially, the fundamental principles of the NLMS algorithm adopted in the adaptive filter are analysed. Then, the NLMS algorithm is applied to analyse the signal processing of the CFM at different flow rates in experiments. By comparing several performance indicators and spectrum diagrams from being filtered by the NLMS algorithm and the least mean square (LMS) algorithm, the results indicate that the NLMS algorithm can lead to a better anti-jamming capability and reduce the influence of noise efficiently for the CFM. In addition, the NLMS method has a faster convergence speed and fewer stable errors than the LMS method. Therefore, the NLMS can improve the quality of the output signal of the CFM.

KW - Adaptive filter

KW - Coriolis mass flowmeter

KW - least mean square

KW - normalized least mean square

KW - signal processing

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JO - Transactions of the Institute of Measurement and Control

JF - Transactions of the Institute of Measurement and Control

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A time-varying signal processing method for Coriolis mass flowmeter based on adaptive filter

Abstract

Keywords

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