基于双声道的低压超声气体流量计数据融合方法

赵伟国,卜勤超,姚海滨,章圣意,章涛

计量学报 ›› 2021, Vol. 42 ›› Issue (7) : 873-878.

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计量学报 ›› 2021, Vol. 42 ›› Issue (7) : 873-878. DOI: 10.3969/j.issn.1000-1158.2021.07.07
流量计量

基于双声道的低压超声气体流量计数据融合方法

  • 赵伟国1,卜勤超1,姚海滨2,章圣意2,章涛1
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A Data Fusion Method of Double-channel Ultrasonic Flowmeter Application in low pressure Gas

  • ZHAO Wei-guo1,BU Qin-chao1,YAO Hai-bin2,ZHANG Sheng-yi2,ZHANG Tao1
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摘要

针对双声道的超声流量测量计测量低压中小管径流量时,超声波信号在低压气体中衰减大、信噪比低,导致某一声道的测量数据产生较大误差或错误,从而降低超声气体流量计测量的准确性和稳定性的问题,提出了一种时差法的双声道超声流量计数据融合方法。该方法首先对单一声道的时差数据进行粗大误差剔除和流量计算后,然后对数据进行预估处理获得流量状态,最后采用改进的卡尔曼融合方法计算管道内的平均流量,从而实现双声道气体超声流量计的数据融合和故障的判断。实验证明该方法的测量相对误差和重复性分别为-0.58%和0.21%。

Abstract

The double-channel ultrasonic flow measurement is conventionally applied in low-pressure gas of the small and medium pipes. However, the error data and wrong data of one channel will be produced by the high attenuation and low signal-to-noise ratio when the ultrasonic signals travel in the low-pressure gas. So the accuracy and stability of ultrasonic flowmeter will be decreased. In order to solve these problems, a fusion method of double-channel ultrasonic flowmeter based on time-difference is proposed. Firstly, the gross errors of time-difference data are eliminated and flowrate is calculated in every channel. Then, the state of the flowrate is estimated by time-difference data. Finally, the improved Kalman data fusion is adopted to calculate the mean flowrate in pipe. The experimental results show that the measurement error is -0.58% and the repeatability is 0.21%.

关键词

计量学 / 双声道超声波流量计 / 时差法 / 卡尔曼融合 / 低压气体 / 状态预估

Key words

metrology / double-channel ultrasonic flowmeter / time-difference method / Kalman fusion / low pressure gas / state estimation

引用本文

导出引用
赵伟国,卜勤超,姚海滨,章圣意,章涛. 基于双声道的低压超声气体流量计数据融合方法[J]. 计量学报. 2021, 42(7): 873-878 https://doi.org/10.3969/j.issn.1000-1158.2021.07.07
ZHAO Wei-guo,BU Qin-chao,YAO Hai-bin,ZHANG Sheng-yi,ZHANG Tao. A Data Fusion Method of Double-channel Ultrasonic Flowmeter Application in low pressure Gas[J]. Acta Metrologica Sinica. 2021, 42(7): 873-878 https://doi.org/10.3969/j.issn.1000-1158.2021.07.07
中图分类号: TB937   

参考文献

[1]鲍敏. 影响气体超声波流量计计量精度的主要因素研究 [D]. 杭州: 浙江大学, 2004.
[2]张皎丹, 郑丹丹, 张涛, 等. 多声道超声流量计数值积分方法优化 [J]. 化工自动化及仪表, 2015, 42 (2): 144-147+238.
Zhang J D, Zheng D D, Zhang T, et al. Optimization of Numerical Integration Method for Multi-path Ultrasonic Flowmeter[J]. Chinese Journal of Scientific Instrument, 2015, 42 (2): 144-147+238.
[3]张彦楠, 杨彬. 加权数据融合方法在多声道超声波流量计测量中的应用 [J]. 传感技术学报, 2017, 30 (12): 1959-1964.
Zhang Y N, Yang B. Application of Weighted Data Fusion Method in Multi-Path Ultrasonic Flowmeter Meas-urement[J]. Chinese Journal of Sensors and Actuators, 2017, 30 (12): 1959-1964.
[4]胡鹤鸣, 孟涛, 王池. 扰流流场对超声流量计积分误差的影响分析[J]. 计量学报, 2011, 32(3): 198-202.
Hu H M, Meng T,Wang C. Theoretical Analysis of Integration Error of Ultrasonic Flowmeter in the Disturbed Flow Condition[J]. Acta Metrologica Sinica, 2011, 32(3): 198-202.
[5]Zhao H, Peng L, Takahashi T, et al. Support Vector Regression-Based Data Integration Method for Multipath Ultrasonic Flowmeter [J]. IEEE Transactions on Instru-mentation and Measurement, 2014, 63 (12): 2717-2725.
[6]许景波, 聂家立, 王升, 等. 基于粗大误差估计的表面测量稳健滤波方法研究[J]. 计量学报, 2017, 38(4): 391-395.
Xu J B, Nie J L,Wang S,et al. A Robust Gaussian Filtering Method in Surface Measurement Based on Gross Error Estimation[J]. Acta Metrologica Sinica, 2017, 38(4): 391-395.
[7]肖明耀. 误差理论与应用 [M]. 北京: 计量出版社, 1985.
[8]唐亚鹏. 基于自适应加权数据融合算法的数据处理 [J]. 计算机技术与发展, 2015, 25 (4): 53-56.
Tang Y P. Data Processing Based on Adaptive Weighted Data Fusion Algorithm [J]. Computer Technology and Development, 2015, 25 (4): 53-56.
[9]刘博, 徐科军, 穆立彬, 等. 基于Kalman滤波的气体超声波流量计融合方法 [J]. 计量学报, 2018, 39 (6):868-873.
Liu B, Xu K J, Mu L B, et al. Fusion Method of Ultrasonic Gas Flowmeter Based on Kalman Filter [J]. Acta Metrologica Sinica, 2018, 39 (6):868-873.
[10]Mahmoud M S, Khalid H M. Distributed Kalman filtering: a bibliographic review [J]. IET Control Theory & Applications, 2013, 7 (4): 483-501.
[11]Wang Y Q, Zhao D, Li Y Y, et al. Unbiased Minimum Variance Fault and State Estimation for Linear Discrete Time-Varying Two-Dimensional Systems [J]. IEEE Transactions on Automatic Control, 2017, 62 (10): 5463-5469.
[12]张品, 董为浩, 高大冬. 一种优化的贝叶斯估计多传感器数据融合方法 [J]. 传感技术学报, 2014, 27 (5): 643-648.
Zhang P, Dong W H, Gao D D. An Optimal Method of Data Fusion for Multi-Sensors Based on Bayesian Estimation [J]. Chinese Journal of Sensors and Actuators, 2014, 27 (5): 643-648.
[13]李鹏飞. 两相流多传感器数据卡尔曼滤波融合方法 [D]. 天津: 天津大学, 2017.
[14]JJG 1030—2007超声流量计[S]. 2007.
[15]穆立彬, 徐科军, 刘博, 等. 基于可变阈值和过零检测的四声道气体超声波流量变送器 [J]. 计量学报, 2019, 40 (2):266-271.
Mu L B, Xu K J, Liu B, et al. Development of Four-channel Ultrasonic Gas Flow Transmitter Based on Variable Threshold and Zero-Crossing Detection[J]. Acta Metrologica Sinica, 2019, 40 (2):266-271.

基金

国家市场监督管理总局科技计划项目(2019MK146)

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