Application of EEMD combined with cross-correlation algorithm in Doppler flow signal

Abstract

To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters, a novel signal processing algorithm that combines ensemble empirical mode decomposition (EEMD) and cross-correlation algorithm was proposed . Firstly, a fast Fourier transform (FFT) spectrum analysis was utilized to ascertain the frequency range of the signal. Secondly, data acquisition was conducted at an appropriate sampling frequency, and the acquired Doppler flow rate signal was then decomposed into a series of intrinsic mode functions (IMFs) by EEMD. Subsequently, these decomposed IMFs were recombined based on their energy entropy, and then the noise of the recombined Doppler flow rate signal was removed by cross-correlation filtering. Finally, an ideal ultrasonic Doppler flow rate signal was extracted. Simulation and experimental verification show that the proposed Doppler flow signal processing method can effectively enhance the signal-to-noise ratio (SNR) and extend the lower limit of measurement of the ultrasonic Doppler flow meter.

Keywords

ultrasonic Doppler flow meter / ensemble empirical mode decomposition (EEMD) / cross-correlation / fast Fourier transform (FFT) spectrum analysis / energy entropy

References

[1] FANG L D, MA X Y, ZHAO J X, et al. Development of a high-precision and wide-range ultrasonic water meter. Flow Measurement and Instrumentation, 2022, 84: 102118.

[2] LYNNWORTH L C, LIU Y. Ultrasonic flowmeters: Half-century progress report, 1955-2005. Ultrasonics, 2006, 44: e1371-e1378.

[3] YI B, ZHANG F X. Ultrasonic flow measuring and it development. Journal of Test and Measurement Technique, 1995, 9(1): 14-18.

[4] XU X L, YANG D Y, CHEN J. Design of ultrasonic Doppler flowmeter. Instrumentation Technology and Sensors, 2016(3): 41-43.

[5] ROSHANAEI S H, ASKARI MOGHADAM R, TARVIRDIZADEH B, et al. Theoretical and experimental evaluation of small flow rate ultrasonic flowmeter. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2022, 44(8): 323.

[6] SUÑOL F, OCHOA D A, GARCIA J E. High-precision time-of-flight determination algorithm for ultrasonic flow measurement. IEEE Transactions on Instrumentation and Measurement, 2019, 68(8): 2724-2732.

[7] TIAN L, XU K J, MU L B, et al. Energy peak fitting of echo based signal processing method for ultrasonic gas flow meter. Measurement, 2018, 117: 41-48.

[8] HAMOUDA A, MANCK O, HAFIANE M L, et al. An enhanced technique for ultrasonic flow metering featuring very low jitter and offset. Sensors, 2016, 16(7): 1008.

[9] GRYSHANOVA I, RAK A, KOROBKO I. The investigation of the correction factor for ultrasonic flow meters. Measurement, 2023, 219: 1-11.

[10] MURAKAWA H, ICHIMURA S, SUGIMOTO K, et al. Evaluation method of transit time difference for clamp-on ultrasonic flowmeters in two-phase flows. Experimental Thermal and Fluid Science, 2020, 112: 109957.

[11] SUN S G, PANG Y, WANG J Q, et al. EEMD harmonic detection method based on the new wavelet threshold denoising pretreatment. Power System Protection and Control, 2016, 44(2): 42-48.

[12] CHENG J S, YU D J, YANG Y. A fault diagnosis approach for roller bearings based on EMD method and AR model. Mechanical Systems and Signal Processing, 2006, 20(2): 350-362.

[13] SHRIVASTAVA Y, SINGH B. A comparative study of EMD and EEMD approaches for identifying chatter frequency in CNC turning. European Journal of Mechanics-A/Solids, 2019, 73: 381-393.

[14] WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.

[15] ZHAO H M, SUN M, DENG W, et al. A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy, 2016, 19(1): 14.

[16] WANG Z Y, QIU J, LI F F. Hybrid models combining EMD/EEMD and ARIMA for long-term streamflow forecasting. Water, 2018, 10(7): 853.

[17] LIU D, LIAO X, OUYANG S, et al. Combined EEMD with a novel flexible wavelet threshold function for weighing signal denoising approach. Journal of Sensors, 2022, 2022: 5314532.

[18] XIAO Y C, KANG N, HONG Y, et al. Misalignment fault diagnosis of DFWT based on IEMD energy entropy and PSO-SVM. Entropy, 2017, 19(1): 6.

[19] HUAI Q, LIU K, QIN L, et al. Backup-protection scheme for multi-terminal HVDC system based on wavelet-packet-energy entropy. IEEE Access, 2019, 7: 49790-49803.

[20] KLAUSEN A, ROBBERSMYR K G. Cross-correlation of whitened vibration signals for low-speed bearing diagnostics. Mechanical Systems and Signal Processing, 2019, 118: 226-244.

[21] GODFREY NNABUIFE S, KUANG B Y, WHIDBORNE J F, et al. Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline riser using a Doppler ultrasonic sensor and deep neural networks. Chemical Engineering Journal, 2021, 403: 126401.

[22] NGUYEN T H L, PARK S. Multi-angle liquid flow measurement using ultrasonic linear array transducer. Sensors, 2020, 20(2): 388.