基于神经网络的超声波流量测量及温度补偿系统的研究
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摘要
传统超声波流量测量中,温度对声速的影响较大,管道内外径、管道压力等流体相关环境因素也会影响超声波的测量精度,为了保证超声波流量计的高准确度和高灵敏度测量,本文研究基于神经网络的超声波流量测量及温度补偿系统。
根据被测流体的流场分布,通过分析流动性对测量的影响因素,确定利用超声波测量流量的时差法工作原理。
针对温度变化引起声速变化的特点,通过对神经网络特性、结构、算法等内容的研究,确立利用神经网络技术来进行温度补偿,并建立超声波流量测量温度补偿模型。
完成了具有温度补偿的超声波流量测量系统硬件设计,硬件电路设计在保证测量精度的前提下,对各个相关电路进行合理的设计。主要硬件电路包括超声波发射电路、接收电路、温度测量电路、系统控制电路、高频振荡和计数电路等。
根据相应的系统硬件结构,进行了软件程序设计。并利用MATLAB仿真软件使用测量样本对网络进行训练,以此对超声流量测量系统进行温度补偿。
利用神经网络技术进行温度补偿,达到了减小误差、提高测量准确度的目的。神经网络技术的应用极大提高了超声波流量测量精度,实现了超声波流量测量温度补偿智能化,为高精度超声波流量计的实际应用奠定了基础。
In traditional ultrasonic flow measurement, temperature has greater infection on the speed of sound, furthermore, the accuracy of ultrasonic will be affected by the related environmental factors of fluid, which include internal diameters and external diameter of the pipeline and pipeline pressure etc. In order to ensure the high accuracy and high sensitivity measurements of ultrasonic flowmeter, this text researches the ultrasonic flow measurement and temperature compensation based on neural network.
According to the flow field of measured fluid, through analyzing the impact of mobility on the measurement of factors, time difference works of ultrasonic flow measurement is determined to use.
According to the characteristics of sound velocity variation caused by temperature variation, through the research of the characteristics, the structure, algorithms and other contents of neural network, determining to use neural network technology to carry out temperature compensation, and temperature compensation model of ultrasonic flow measurement is established.
Ultrasonic flow measurement system hardware design with temperature compensation is completed. Related circuits are designed rationally by the premise of ensuring accuracy of hardware design. Major hardware circuits include ultrasonic transmitter circuit, receiver circuit, temperature measurement circuit, system control circuit, high frequency oscillation and counting circuit.
The software programs are designed according to the corresponding hardware structure. Using MATLAB simulation software to train the network by using measurement samples, thus temperature compensation of the ultrasonic flow measurement system is established.
By using neural network technology to temperature compensation, the purpose for reducing error and increasing measurement accuracy are attained. The precision of the ultrasonic flow measurement is improved greatly by the application of neural network technique, and the intelligent temperature compensation of the ultrasonic flow measurement is realized, which lays the foundation for the practical application of high precision ultrasonic flowmeter.
根据被测流体的流场分布,通过分析流动性对测量的影响因素,确定利用超声波测量流量的时差法工作原理。
针对温度变化引起声速变化的特点,通过对神经网络特性、结构、算法等内容的研究,确立利用神经网络技术来进行温度补偿,并建立超声波流量测量温度补偿模型。
完成了具有温度补偿的超声波流量测量系统硬件设计,硬件电路设计在保证测量精度的前提下,对各个相关电路进行合理的设计。主要硬件电路包括超声波发射电路、接收电路、温度测量电路、系统控制电路、高频振荡和计数电路等。
根据相应的系统硬件结构,进行了软件程序设计。并利用MATLAB仿真软件使用测量样本对网络进行训练,以此对超声流量测量系统进行温度补偿。
利用神经网络技术进行温度补偿,达到了减小误差、提高测量准确度的目的。神经网络技术的应用极大提高了超声波流量测量精度,实现了超声波流量测量温度补偿智能化,为高精度超声波流量计的实际应用奠定了基础。
In traditional ultrasonic flow measurement, temperature has greater infection on the speed of sound, furthermore, the accuracy of ultrasonic will be affected by the related environmental factors of fluid, which include internal diameters and external diameter of the pipeline and pipeline pressure etc. In order to ensure the high accuracy and high sensitivity measurements of ultrasonic flowmeter, this text researches the ultrasonic flow measurement and temperature compensation based on neural network.
According to the flow field of measured fluid, through analyzing the impact of mobility on the measurement of factors, time difference works of ultrasonic flow measurement is determined to use.
According to the characteristics of sound velocity variation caused by temperature variation, through the research of the characteristics, the structure, algorithms and other contents of neural network, determining to use neural network technology to carry out temperature compensation, and temperature compensation model of ultrasonic flow measurement is established.
Ultrasonic flow measurement system hardware design with temperature compensation is completed. Related circuits are designed rationally by the premise of ensuring accuracy of hardware design. Major hardware circuits include ultrasonic transmitter circuit, receiver circuit, temperature measurement circuit, system control circuit, high frequency oscillation and counting circuit.
The software programs are designed according to the corresponding hardware structure. Using MATLAB simulation software to train the network by using measurement samples, thus temperature compensation of the ultrasonic flow measurement system is established.
By using neural network technology to temperature compensation, the purpose for reducing error and increasing measurement accuracy are attained. The precision of the ultrasonic flow measurement is improved greatly by the application of neural network technique, and the intelligent temperature compensation of the ultrasonic flow measurement is realized, which lays the foundation for the practical application of high precision ultrasonic flowmeter.
引文
[1]黄建军.关于改进超声波流量计性能的研究[D].沈阳工业大学,2002
[2]刘佰英,管道超声波流量计研究[D].东北大学,2005
[3] Mcshane J.L. Ultrasonic Flowmeter Basics[J].Instrumentation Technology,1979,7
[4]杨德旭,基于BP神经网络传感器温度补偿技术的研究[D],东北大学,2006
[5]蔡武昌,应启戛.新型流量检测仪表.北京:化学工业出版社,2006
[6]超声波流量计,东方电子集成商务网,产品中心热工测量仪表/超声波流量计,2005
[7]魏守包.基ARM的时差法超声波流量计研制[D].南京信息工程大学,2008
[8]陈静.超声波流量计的研制[D].西安科技大学,2004
[9]刘欣荣.流量计(第二版).水利电力出版社,1990
[10]何群.超声波流量测量系统的关键技术研究与实现[D].哈尔滨工程大学,2004
[11] Daniel Graupe, Principles of Artificial Neural Networks.World Scientific Publishing Co.,1997:26
[12]彭黎辉,张宝芬,姚丹亚,基于模糊神经元网络的两相流流型辩识方法,模式识别与人工智能,1997(4):332-337
[13] Maccormack MD, Neural Computing in Geophysics, Geophysics:TLD, 1991, 10(1):11-14
[14] Lei Zhang, M.Akiyama, Kai Huang, H.Sugiyama, and N.Ninomiya. Estimation of Flow Patterns by Applying Artificial Neural Networks. 1996 IEEE international Conference on System, Man and Cybernetics(Vol.2), 1996.10
[15]左瑞芹,光纤陀螺温度补偿技术研究[D].哈尔滨工程大学,2006
[16]梁胜,基于微处理器的磁敏角位移位传感器温度补偿系统的设计研究[D].华南师范大学,2007
[17]韩小菊,声表面波加速度计的温度误差及补偿方法研究[D].西安科技大学,2009
[18]刘杨,振弦式传感器温度补偿研究[D].长沙理工大学,2009
[19]张伟,陈文义.流体力学[M].天津大学出版社,2009
[20]沈世镒.神经网络系统理论及其应用[M].科学出版社,2000
[21] Hagan,M.T.等著,戴葵等译.神经网络设计[M].机械工业出版社,2002.9
[22]严太山,神经网络BP算法研究及其在工业检测中的应用[D].广西师范大学,2001
[23] Lynnworth L C,Liu Y. Ultrasonic flowmeter:falf-century progress report,1955-2005. Ultrasonic,2006,44(5):e1371-e1378
[24] E. Luntta, J. Halttunen,“Neural network approach to ultrasonic flow measurements”, Flow Measurement and Instrumentation, Tampere Finland, 1999, 35-43
[25] Neil White,“Intelligent sensors”, Sensor Review,1997, 97-98
[26]赵正琦,BP神经网络优化及其在流体测量中的应用[D].北京化工大学,2000
[27]陈希,付兴武,张兴元,陈峰. BP神经网络在测距误差补偿中的应用[J].工矿自动化,2008(3):47-48
[28]贺清碧. BP神经网络及应用研究[D].重庆交通学院.2004
[29]朱庆华. BP多层神经网络在控制中的应用[D].广西大学,2004
[30]张朋,陈明,秦波,何鹏举.基于BP神经网络的振动筒压力传感器的温度补偿[J].传感技术学报,2007(20):2213-2217
[31]高隽著.人工神经网络原理及仿真实例[M].机械工业出版社,2007
[32] Prtra J C. An artificial neural network based smart capacitive pressure sensor[J]. Measurement,1997,22(3-4):113-121
[33]敬忠良,神经网络跟踪理论及应用[M].北京:国防工业出版社,1995
[34] Prtra J C,Pal R N. A functional link artificial neural network for adaptive channel equalization[J]. Signal Processing,1995,43(2):181-195
[35] M. Willatzen,“Temperature gradients and flowmeter performance”, Ultrasonics, Denmark, 2001, 383-389
[36] Looney C G.Advances in Feedforward Neural Network Training.IEEE Computer,1996,3:45-54
[37]王艳霞,傅星,胡小唐,利用超声波检测流量的高精度系统[J].天津大学学报,2003,248-250
[38]马戎,周王民,陈明.声表面波压力传感器的温度补偿[J].传感器技术,2005.24(2):37-42
[39] Mandard E,Kouame D,Battault Retal.Transit time ultrasonic flowmeter:velocity profile estimation.Ultrasonic symposium,2005,2(9):18-21
[40]闻新,周露等著.MATLAB神经网络仿真与应用[M].科学出版社,2003
[41] Simon Haykin著,叶世伟等译.神经网络原理[M].机械工业出版社,2004
[42] ATMEL Corporation Datasheet. AT89S51-8-bit Microcontroller with 4Kbytes Flash[S].2000
[43]孙育才.MCS-51系列单片微型计算机及其应用(第三版)[M] .东南大学出版社,1997
[44] Mzard,M.,Nadal,J.PP.Learning in Feed-forward Layered Networks:The Tiling Algorithm[J].J.Phys.A:Math.Gen.,1989,22(8):2192-2203
[45] T.T.Yeh and P.I.Espina.An Intelligent Ultrasonic Flow Meter for Improved Flow Measurement and Flow Calibration Facility. IEEE Instrumentation and Measurement Technology Conference Budapest, Hungary, May 21-23,2001.
[46] T.T.Yeh, G.E.Mattingly. Computer Simulations of Ultrasonic Flow Meter Performance in Ideal and Non-ideal Pipeflows. Proceedings of the 1997 ASME Fluids Engineering Division Summer Meeting(Vancouver,1997),Vancouver,BC,1997,FEDSM97-3012
[47]樊桂花.基于人工神经网络的传感器静态特性拟合[J],装备指挥技术学院学报,2002,ll(6),74-76.
[48] Aase.SO, Ruoff.P.Semi-algebraic optimization of temperature compensation in a general switch-type negative feedback model of circadian clocks[J]. Journal of Mathematical Biology, 2008vol.56(no.3)
[49] D.J.Field. Relations between the statisties of natural images and the response properties of cortical cells. Journal of the Optical Society of American.1987 A4(12):2379~2394
[50] David D.zhang,Neural Networks System Design Methodology,Tsinghua University Press.1996:1-2
[51] Y.S.Huang, M.S.Young. An Accurate Ultrasonic Distance Measurement System with Self Temperature Compensation. Instrumentation science & technology, 2009vol.37(no.1)
[52] Yan-xia Wang, Zhi-hao Li,“Temperature Compensation of Ultrasonic Flow Measurement Based on the Neural Network”, The 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI'09)
[53] Luntta E,Halttunen J.Neural network approach to ultrasonic flow measurements[J].Flow Measurement and Instrumentation,1999,10(1):35-43
[54] Jabbari, Amir, Jedermann, Lang, Walter, Neural network based data fusion in food transportation system, Proceedings of the 11th International Conference on Information Fusion, 2008, 4:352.356
[2]刘佰英,管道超声波流量计研究[D].东北大学,2005
[3] Mcshane J.L. Ultrasonic Flowmeter Basics[J].Instrumentation Technology,1979,7
[4]杨德旭,基于BP神经网络传感器温度补偿技术的研究[D],东北大学,2006
[5]蔡武昌,应启戛.新型流量检测仪表.北京:化学工业出版社,2006
[6]超声波流量计,东方电子集成商务网,产品中心热工测量仪表/超声波流量计,2005
[7]魏守包.基ARM的时差法超声波流量计研制[D].南京信息工程大学,2008
[8]陈静.超声波流量计的研制[D].西安科技大学,2004
[9]刘欣荣.流量计(第二版).水利电力出版社,1990
[10]何群.超声波流量测量系统的关键技术研究与实现[D].哈尔滨工程大学,2004
[11] Daniel Graupe, Principles of Artificial Neural Networks.World Scientific Publishing Co.,1997:26
[12]彭黎辉,张宝芬,姚丹亚,基于模糊神经元网络的两相流流型辩识方法,模式识别与人工智能,1997(4):332-337
[13] Maccormack MD, Neural Computing in Geophysics, Geophysics:TLD, 1991, 10(1):11-14
[14] Lei Zhang, M.Akiyama, Kai Huang, H.Sugiyama, and N.Ninomiya. Estimation of Flow Patterns by Applying Artificial Neural Networks. 1996 IEEE international Conference on System, Man and Cybernetics(Vol.2), 1996.10
[15]左瑞芹,光纤陀螺温度补偿技术研究[D].哈尔滨工程大学,2006
[16]梁胜,基于微处理器的磁敏角位移位传感器温度补偿系统的设计研究[D].华南师范大学,2007
[17]韩小菊,声表面波加速度计的温度误差及补偿方法研究[D].西安科技大学,2009
[18]刘杨,振弦式传感器温度补偿研究[D].长沙理工大学,2009
[19]张伟,陈文义.流体力学[M].天津大学出版社,2009
[20]沈世镒.神经网络系统理论及其应用[M].科学出版社,2000
[21] Hagan,M.T.等著,戴葵等译.神经网络设计[M].机械工业出版社,2002.9
[22]严太山,神经网络BP算法研究及其在工业检测中的应用[D].广西师范大学,2001
[23] Lynnworth L C,Liu Y. Ultrasonic flowmeter:falf-century progress report,1955-2005. Ultrasonic,2006,44(5):e1371-e1378
[24] E. Luntta, J. Halttunen,“Neural network approach to ultrasonic flow measurements”, Flow Measurement and Instrumentation, Tampere Finland, 1999, 35-43
[25] Neil White,“Intelligent sensors”, Sensor Review,1997, 97-98
[26]赵正琦,BP神经网络优化及其在流体测量中的应用[D].北京化工大学,2000
[27]陈希,付兴武,张兴元,陈峰. BP神经网络在测距误差补偿中的应用[J].工矿自动化,2008(3):47-48
[28]贺清碧. BP神经网络及应用研究[D].重庆交通学院.2004
[29]朱庆华. BP多层神经网络在控制中的应用[D].广西大学,2004
[30]张朋,陈明,秦波,何鹏举.基于BP神经网络的振动筒压力传感器的温度补偿[J].传感技术学报,2007(20):2213-2217
[31]高隽著.人工神经网络原理及仿真实例[M].机械工业出版社,2007
[32] Prtra J C. An artificial neural network based smart capacitive pressure sensor[J]. Measurement,1997,22(3-4):113-121
[33]敬忠良,神经网络跟踪理论及应用[M].北京:国防工业出版社,1995
[34] Prtra J C,Pal R N. A functional link artificial neural network for adaptive channel equalization[J]. Signal Processing,1995,43(2):181-195
[35] M. Willatzen,“Temperature gradients and flowmeter performance”, Ultrasonics, Denmark, 2001, 383-389
[36] Looney C G.Advances in Feedforward Neural Network Training.IEEE Computer,1996,3:45-54
[37]王艳霞,傅星,胡小唐,利用超声波检测流量的高精度系统[J].天津大学学报,2003,248-250
[38]马戎,周王民,陈明.声表面波压力传感器的温度补偿[J].传感器技术,2005.24(2):37-42
[39] Mandard E,Kouame D,Battault Retal.Transit time ultrasonic flowmeter:velocity profile estimation.Ultrasonic symposium,2005,2(9):18-21
[40]闻新,周露等著.MATLAB神经网络仿真与应用[M].科学出版社,2003
[41] Simon Haykin著,叶世伟等译.神经网络原理[M].机械工业出版社,2004
[42] ATMEL Corporation Datasheet. AT89S51-8-bit Microcontroller with 4Kbytes Flash[S].2000
[43]孙育才.MCS-51系列单片微型计算机及其应用(第三版)[M] .东南大学出版社,1997
[44] Mzard,M.,Nadal,J.PP.Learning in Feed-forward Layered Networks:The Tiling Algorithm[J].J.Phys.A:Math.Gen.,1989,22(8):2192-2203
[45] T.T.Yeh and P.I.Espina.An Intelligent Ultrasonic Flow Meter for Improved Flow Measurement and Flow Calibration Facility. IEEE Instrumentation and Measurement Technology Conference Budapest, Hungary, May 21-23,2001.
[46] T.T.Yeh, G.E.Mattingly. Computer Simulations of Ultrasonic Flow Meter Performance in Ideal and Non-ideal Pipeflows. Proceedings of the 1997 ASME Fluids Engineering Division Summer Meeting(Vancouver,1997),Vancouver,BC,1997,FEDSM97-3012
[47]樊桂花.基于人工神经网络的传感器静态特性拟合[J],装备指挥技术学院学报,2002,ll(6),74-76.
[48] Aase.SO, Ruoff.P.Semi-algebraic optimization of temperature compensation in a general switch-type negative feedback model of circadian clocks[J]. Journal of Mathematical Biology, 2008vol.56(no.3)
[49] D.J.Field. Relations between the statisties of natural images and the response properties of cortical cells. Journal of the Optical Society of American.1987 A4(12):2379~2394
[50] David D.zhang,Neural Networks System Design Methodology,Tsinghua University Press.1996:1-2
[51] Y.S.Huang, M.S.Young. An Accurate Ultrasonic Distance Measurement System with Self Temperature Compensation. Instrumentation science & technology, 2009vol.37(no.1)
[52] Yan-xia Wang, Zhi-hao Li,“Temperature Compensation of Ultrasonic Flow Measurement Based on the Neural Network”, The 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI'09)
[53] Luntta E,Halttunen J.Neural network approach to ultrasonic flow measurements[J].Flow Measurement and Instrumentation,1999,10(1):35-43
[54] Jabbari, Amir, Jedermann, Lang, Walter, Neural network based data fusion in food transportation system, Proceedings of the 11th International Conference on Information Fusion, 2008, 4:352.356