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.

Comparative study of phase difference extraction methods in the background of Coriolis mass flow meters application

“…With the development of computer science and semiconductor technology, digital signal processing methods have gradually become the mainstream and are widely used in all walks of life. At present, scholars have done a lot of research work on digital signal processing methods [3][4][5], and proposed a variety of phase difference calculation methods.…”