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.
AnklinMDrahmWRiederA (2006) Coriolis mass flowmeters: overview of the current state of the art and latest research. Flow Measurement and Instrumentation17(6): 317–323.
2.
CascettaFCignoloGGoriaRet al. (1992) Experimental intercomparison of Coriolis mass flowmeters. Transactions of the Institute of Measurement and Control14(2): 99–107.
3.
CheesewrightRClarkCBissetD (1999) Understanding the experiment: a response of Coriolis massflow meters to flow pulsations. Flow Measurement and Instrumentation10: 207–215.
4.
ClarkCCheesewrightR (2003) The influence upon Coriolis mass flow meters of external vibrations at selected frequencies. Flow Measurement and Instrumentation14: 33–42.
5.
DebiasiMYanPengÖzbayHet al. (2009) Seven tuning schemes for an ADALINE model to predict floor pressures in a subsonic cavity flow. Transactions of the Institute of Measurement and Control31(1): 97–112.
6.
HaneveldJLammerinkTSJde BoerMJet al. (2010) Modeling, design, fabrication and characterization of a micro Coriolis massflow. Sensor Journal of Micromechanics and Microengineering20: 1–10.
HongFDuCTeeKPet al. (2010) Adaptive disturbance rejection in the presence of uncertain resonance mode in hard disk drives. Transactions of the Institute of Measurement and Control32(2): 99–119.
9.
HouQi-LiXuKe-JunFangMinet al. (2013) Development of Coriolis mass flowmeter with digital drive and signal processing technology. ISA Transactions52: 692–700.
10.
HuChunZhengDezhiFanShangchun (2013) Experimental study and implementation of a novel digital closed-loop control system for Coriolis mass flowmeter. IEEE Sensors Journal13(8): 3032–3038.
11.
HuChunZhengDezhiFanShangchunet al. (2012) Research and implementation of amplitude ratio. In: The 8th IEEE International Symposium on Instrumentation and Control Technology, London, UK, 11–13 July, pp. 147–152. Institute of Electrical and Electronics Engineers.
12.
LiuRPFuentMJHenryMPet al. (2001) A neural network to correct mass flow errors caused by two-phaseflow in a digital Coriolis mass flowmeter. Flow Measurement and Instrumentation12: 53–63.
13.
LiuSongmingShiHongweiYanJianget al (2012) An interpolation FFT algorithm with high accuracy. Advanced Materials Research524–527: 3838–3844.
14.
MaYEidenschinkT (2001) Motion induced signals of Coriolis flowmeters. Flow Measurement and Instrumentation. 12(3): 213–217.
15.
NiuPenghuiTuYaqing (2006) A method for signal processing of Coriolis mass flowmeter based on combining AR. Instrumentation and Measurement Technology Conference: 1417–1420.
16.
ShenTing-AoTuYa-QingZhangHai-Tao (2014) A novel time varying signal processing method for Coriolis mass flowmeter. Review of Scientific Instruments85: 065116.
17.
SoriaFRCBadilloIATorresGFet al (2013) A simplification to the fast FIR-FFT filtering technique in the DSP interpolation process for band-limited signals. Revista Facultad de Ingenieria-universidad de Antioquia68: 9–19.
18.
WangLJHuLZhuZCet al. (2011) Analytical calculation of sensitivity for Coriolis mass flowmeter. Measurement44: 1117–1127.
19.
WangTHussainY (2010) Pressure effects on Coriolis mass flowmeters. Flow Measurement and Instrumentation21: 504–510.
20.
XuKejunJiangHankeSuJianhui (1999) Research on frequency tracking method in signal processing of Coriolis mass flowmeter. Acta Metrologica Sinica4(19): 304–307.
21.
YamadaT (2013) High-accuracy estimations of frequency, amplitude, and phase with a modified DFT for asynchronous sampling. IEEE Transactions on Instrumentation and Measurement62(6): 1428–1435.
22.
ZengZhaofuZhaoQiaohong (2010) MATLAB simulation and DSP realization of normalized LMS adaptive filter. Journal of Hunan University of Science & Technology3(25): 67–71.
23.
ZhangBingHuHong (2010) An improved window and interpolation algorithm using trispectrum for measuring power harmonics based on FFT. In: 2010 First International Conference on Pervasive Computing, Signal Processing and Applications. Harbin, China, 11–19 September, pp. 491–494.
24.
ZhengDezhiWangShuaiFanShangchun (2009) Nonlinear vibration characteristics of Coriolis mass flowmeter. Chinese Journal of Aeronautics22: 198–205.
