JP3036217B2 - Flowmeter - Google Patents
FlowmeterInfo
- Publication number
- JP3036217B2 JP3036217B2 JP4066775A JP6677592A JP3036217B2 JP 3036217 B2 JP3036217 B2 JP 3036217B2 JP 4066775 A JP4066775 A JP 4066775A JP 6677592 A JP6677592 A JP 6677592A JP 3036217 B2 JP3036217 B2 JP 3036217B2
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- Japan
- Prior art keywords
- flow rate
- flow
- signal
- pulse
- neural network
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Description
【0001】[0001]
【産業上の利用分野】本発明は、都市ガス、LPGガス
等の気体や液体等の流体流量を計測する流量計に係わ
り、特に高精度の演算機能を有する流量計に関するもの
である。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a flow meter for measuring a flow rate of a fluid such as a gas or a liquid such as city gas or LPG gas, and more particularly to a flow meter having a high-precision arithmetic function.
【0002】[0002]
【従来の技術】従来、この種の流量計は、例えば特開平
3−95420号公報に示されているように、図3、図
4のような構成になっていた。2. Description of the Related Art Conventionally, this type of flow meter has a configuration as shown in FIGS. 3 and 4, for example, as disclosed in Japanese Patent Application Laid-Open No. 3-95420.
【0003】即ち、図3の従来の流量計において、1は
流量計で、2はガス配管、3はフルイディック発振素子
で、流体のもつ運動エネルギーを利用して流体発振を生
じさせる。4はセンサーで、流体発振の周波数を検出す
る。5は遮断弁で、異常な使用状態を検出するとガスの
供給を遮断する。6は制御装置で図4にその一例を示
す。That is, in the conventional flow meter of FIG. 3, 1 is a flow meter, 2 is a gas pipe, and 3 is a fluidic oscillation element, which generates fluid oscillation by utilizing kinetic energy of the fluid. A sensor 4 detects the frequency of fluid oscillation. Reference numeral 5 denotes a shutoff valve which shuts off gas supply when an abnormal use state is detected. Reference numeral 6 denotes a control device, an example of which is shown in FIG.
【0004】図4において7はアナログ増幅器で、セン
サー4で検出した流量信号を増幅する。8は波形整形回
路で、増幅した信号をパルス信号に変換する。9は立ち
上がり点検出回路で、流量パルス信号の立ち上がりを検
出する。10は周期測定手段で、流量パルスの立ち上が
り点から次の立ち上がり点までの時間、即ち周期を計測
する。11は記憶回路で、パルス定数と流量あるいは周
期の関係はn個の折れ線で近似しており、n個の折れ線
の境界の周期を記憶する手段12と、流量パルスの周期
より短い単位時間tを記憶する単位時間記憶手段13
と、パルス定数の補正単位量αを記憶する補正単位量手
段14と、定数項aを記憶する定数記憶手段15とから
なる。これらの記憶手段はn個の折れ線の区分に対応し
てn個ずつもうけられている。16は加算回路で、1パ
ルス当りの流量を示すパルス定数K=a+Σαを1周期
毎求め加算する。17は積算回路で、求めた流量を積算
する。18は表示器で、積算した結果を表示する。In FIG. 4, reference numeral 7 denotes an analog amplifier which amplifies a flow signal detected by the sensor 4. A waveform shaping circuit 8 converts the amplified signal into a pulse signal. Reference numeral 9 denotes a rising point detection circuit which detects the rising of the flow pulse signal. Numeral 10 is a cycle measuring means for measuring the time from the rising point of the flow pulse to the next rising point, that is, the cycle. Numeral 11 is a storage circuit, and the relationship between the pulse constant and the flow rate or the cycle is approximated by n polygonal lines. Unit time storage means 13 for storing
And a correction unit amount means 14 for storing a correction unit amount α of the pulse constant, and a constant storage means 15 for storing a constant term a. These storage means are provided n by n in correspondence with the division of the n polygonal lines. Reference numeral 16 denotes an adder circuit for obtaining and adding a pulse constant K = a + Σα indicating a flow rate per pulse for each cycle. Reference numeral 17 denotes an integrating circuit for integrating the obtained flow rates. Reference numeral 18 denotes a display, which displays the integrated result.
【0005】次に、上記従来の動作を図5、図6を用い
て説明する。何等かのガス器具が使用されるとガスはフ
ルイディック発振素子3に入り流体発振が生じ、センサ
ー4よりその流量変化を検出する。その出力信号をアナ
ログ増幅器7で増幅し波形整形回路8でパルス信号に変
換する。Next, the above-mentioned conventional operation will be described with reference to FIGS. When any kind of gas appliance is used, the gas enters the fluid oscillation element 3 to generate fluid oscillation, and the sensor 4 detects a change in the flow rate. The output signal is amplified by an analog amplifier 7 and converted into a pulse signal by a waveform shaping circuit 8.
【0006】流量と発振周波数の関係はQ=a・F+b
で与えられる。これを1パルス当りの流量を求める式K
=Q/F=a+b・Tに変更する。ここで、Kをパルス
定数といい、1パルス当りの流量値を示す。a、bは係
数。パルス定数と流量あるいは振動周波数との関係は図
5に示すように一定ではないため折れ線近似している。
流量パルスの周期が折れ線近似の境界を越えた場合、係
数を変えてパルス定数を演算する。従って係数は折れ線
区分毎に設定されている。またここではb・Tという乗
算処理を行わずに加算処理で行い、且つパルス定数Kを
もとめる。The relationship between the flow rate and the oscillation frequency is Q = a · F + b
Given by This is calculated by the equation K for calculating the flow rate per pulse.
= Q / F = a + b · T. Here, K is called a pulse constant, and indicates a flow value per pulse. a and b are coefficients. Since the relationship between the pulse constant and the flow rate or the oscillation frequency is not constant as shown in FIG.
If the period of the flow pulse exceeds the boundary of the polygonal line approximation, the coefficient is changed to calculate the pulse constant. Therefore, the coefficient is set for each line segment. In this case, the pulse constant K is obtained by performing the addition processing without performing the multiplication processing of b · T.
【0007】この内容を図6を用いて説明する。まず立
ち上がり点検出回路9で波形整形回路8より出力された
流量パルスの立ち上がりを検出する。立ち上がり検出す
ると周期測定手段10で流量パルスの周期を計測開始す
る。同時に加算回路16で次の処理を行う。b・Tの演
算を行う代わりに、b・Tの値よりはるかに小さい単位
補正量αを加算して求める。加算は流量パルスの周期T
より比較的短い時間、単位補正時間t毎に行う。よって
α=b・tといえる。従って流量パルスの1周期、立ち
上がり点から次の立ち上がり点検出するまでの間単位時
間t経過する毎に単位補正量αを加算し続ける。その結
果得られたK=a+Σαが1パルス当りの流量、即ちパ
ルス定数になる。パルス定数と流量パルスの周期との関
係は折れ線近似しているので、それぞれの折れ線区分毎
に係数a、単位補正量α、単位補正時間tをもってい
る。図6では境界周期T1〜T2ではα1、t1、また
T2〜T3ではα2、t2と境界T2を境に変化してい
る。従って、加算回路16では周期測定手段10によっ
て計測した周期が折れ線区分の境界の周期に達したかど
うかを判定し(周期測定手段10はパルスの周期を測定
するとともに境界周期をも測定する)、次の折れ線区分
の領域に入ったならば係数a、単位補正量α、単位補正
時間tを変更して上記処理を継続する。The contents will be described with reference to FIG. First, the rising point detection circuit 9 detects the rising of the flow rate pulse output from the waveform shaping circuit 8. When the rise is detected, the cycle measuring means 10 starts measuring the cycle of the flow pulse. At the same time, the following processing is performed by the addition circuit 16. Instead of performing the calculation of b · T, a unit correction amount α that is much smaller than the value of b · T is added and obtained. The addition is the period T of the flow pulse.
This is performed for each unit correction time t, which is a relatively short time. Therefore, it can be said that α = bt. Therefore, the unit correction amount α is continuously added each time the unit time t elapses from the rising point to the detection of the next rising point in one cycle of the flow pulse. The resulting K = a + Σα is the flow rate per pulse, that is, the pulse constant. Since the relationship between the pulse constant and the cycle of the flow pulse approximates a polygonal line, each polygonal segment has a coefficient a, a unit correction amount α, and a unit correction time t. In FIG. 6, α1 and t1 change at the boundary period T1 to T2, and α2 and t2 change at T2 to T3 at the boundary T2. Therefore, the addition circuit 16 determines whether the period measured by the period measuring unit 10 has reached the boundary period of the polygonal line segment (the period measuring unit 10 measures the pulse period and also measures the boundary period), When the processing enters the area of the next polygonal line section, the coefficient a, the unit correction amount α, and the unit correction time t are changed and the above processing is continued.
【0008】このようにして求めた流量を積算回路17
で加算していくと使用積算値がもとまる。この積算値を
表示器18で表示している。The flow rate obtained in this manner is integrated with the integrating circuit 17.
By adding in, the used integrated value is obtained. This integrated value is displayed on the display 18.
【0009】[0009]
【発明が解決しようとする課題】しかしながら上記従来
の構成では、流量と振動周波数(あるいは周期)の関係
を示すパルス定数を線形近似しているために特に折れ線
の境界近傍では誤差が大きくなり流量を正確に計測でき
ず、また積算流量値にも大きく影響するという課題があ
った。However, in the above-described conventional configuration, since the pulse constant indicating the relationship between the flow rate and the vibration frequency (or period) is linearly approximated, an error becomes large especially near the boundary of the polygonal line, and the flow rate is reduced. There has been a problem that accurate measurement cannot be performed and the integrated flow rate value is greatly affected.
【0010】本発明は上記課題を解決するもので、正確
な流量計測がおこなえる流量計を提供することを目的と
したものである。An object of the present invention is to solve the above-mentioned problems and to provide a flow meter capable of performing accurate flow measurement.
【0011】[0011]
【課題を解決するための手段】本発明は上記目的を達成
するため、流体流量を検出する流量検出手段と、予め検
出して入力された流量入力手段、前記流量入力手段と前
記流量検出手段の出力信号との非線形特性を示す関数を
推定し流量値を求める神経回路網模式手段とを設けたも
のである。In order to achieve the above object, the present invention provides a flow rate detecting means for detecting a fluid flow rate, a flow rate input means previously detected and inputted, and a flow rate detecting means for detecting the fluid flow rate. A neural network model for estimating a function indicating a non-linear characteristic with an output signal and obtaining a flow rate value.
【0012】[0012]
【作用】本発明は上記構成によって、流体流量を検出す
る流量検出手段の出力信号と予め入力された流量入力手
段の流量値とから神経回路網模式手段でまず流量を求め
る関数を推定し、次に求めた関数と流量検出手段の出力
信号とから瞬時流量を演算し、更に積算流量を求める。According to the present invention, a function for obtaining the flow rate is first estimated by the neural network model means from the output signal of the flow rate detection means for detecting the fluid flow rate and the flow rate value of the flow rate input means which has been inputted in advance. The instantaneous flow rate is calculated from the function obtained in (1) and the output signal of the flow rate detection means, and the integrated flow rate is further obtained.
【0013】このように流体流量と流量検出手段の出力
信号との非線形な関数を示す関数を神経回路網模式手段
を用いて推論し、更に推論よって流量を関数と流量検出
手段の出力信号とから求めるので誤差を極めて小さく、
且つ高精度にもとめることができる。その結果ガスの使
用量である積算値も正確に計測できる。As described above, a function indicating a non-linear function of the fluid flow rate and the output signal of the flow rate detection means is inferred using the neural network model means, and the flow rate is further inferred from the function and the output signal of the flow rate detection means. The error is extremely small because
In addition, high accuracy can be obtained. As a result, the integrated value, which is the amount of gas used, can also be accurately measured.
【0014】[0014]
【実施例】以下本発明の実施例を図1、図2を参照して
説明する。図1において、図3と同一の構成要素には同
一の番号を付した。DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to FIGS. 1, the same components as those in FIG. 3 are denoted by the same reference numerals.
【0015】図1は本発明の流量計のブロック図であ
る。図1において、19は流量検出手段で、例えばフル
イディック発振素子3を用いて流体発振を発生させ、流
体の発振周波数を例えば圧電センサー、サーミスタ等を
用いて圧力−電圧変化、熱−抵抗変化として検出した
り、あるいは熱線式センサーにより流速を求めたりす
る。20は流量入力手段で、予めあるいは同時に流量検
出手段19の信号に対応した流量を入力する。FIG. 1 is a block diagram of a flow meter according to the present invention. In FIG. 1, reference numeral 19 denotes a flow rate detecting means, which generates fluid oscillation using, for example, a fluidic oscillation element 3, and changes the oscillation frequency of the fluid as a pressure-voltage change and a heat-resistance change using, for example, a piezoelectric sensor or a thermistor. Detect, or determine the flow velocity with a hot wire sensor. Reference numeral 20 denotes a flow rate input means which inputs a flow rate corresponding to a signal of the flow rate detection means 19 in advance or simultaneously.
【0016】21は神経回路網模式手段で、図2に神経
回路網模式手段の構成を示す。神経回路網模式手段は擬
似シナプス結合変換器20aと、擬似シナプス結合変換
器20aからの出力を加算する加算器20bと、たとえ
ばシグモイド関数を用いて加算器20bの出力を非線形
変換する非線形関数変換器20cと、誤差演算手段20
dと修正手段20eとからなる。流量検出手段19と流
量入力手段20の出力が神経回路網模式手段21に入
り、そこで流量と流量検出手段の関係を示す流量関数を
推論し、つぎに推論した関数と流量検出手段19の出力
信号とでそのときの流量を求める。22は流量積算手段
で、求めた流量を積算し積算値を求める。Reference numeral 21 denotes a neural network model, and FIG. 2 shows the configuration of the neural network model. The neural network model means includes a pseudo-synaptic coupling converter 20a, an adder 20b for adding the output from the pseudo-synaptic coupling converter 20a, and a non-linear function converter for nonlinearly converting the output of the adder 20b using, for example, a sigmoid function. 20c and error calculating means 20
d and correction means 20e. The outputs of the flow rate detecting means 19 and the flow rate inputting means 20 enter the neural network model means 21, where a flow rate function indicating the relationship between the flow rate and the flow rate detecting means is inferred, and then the inferred function and the output signal of the flow rate detecting means 19 are output. And obtain the flow rate at that time. 22 is a flow rate integrating means for integrating the determined flow rates to determine an integrated value.
【0017】次に上記構成の動作を説明する。ガス等が
使用され始めると流体流量を流量検出手段19によって
例えば電圧信号などの信号形態で検出しその検出した信
号及びこのときの流体流量を流量入力手段20から神経
回路網模式手段21に入力する。神経回路網模式手段2
1はこの場合2個の入力を受け1個の出力を出し、ブロ
ックの中は多層パーセプトロンの構成をとっている。神
経回路網模式手段ではまず擬似シナプス結合器20aに
はいり、擬似シナプス結合器20aでは重み結合係数ω
iと入力信号fiの積演算ωi・fiが行われる。擬似
シナプス結合器20aから出力された全信号は加算器2
0bにはいり加算処理y=Σωi・fiされ、非線形変
換器20cに出力され最終信号となる。非線形変換器2
0はシグモイド関数と呼ばれる非線形関数で構成され、
加算器20b出力の信号が入力される。シグモイド関数
は、次式Next, the operation of the above configuration will be described. When the gas or the like starts to be used, the fluid flow rate is detected by the flow rate detecting means 19 in the form of a signal such as a voltage signal, and the detected signal and the fluid flow rate at this time are input from the flow rate input means 20 to the neural network model means 21. . Neural network model 2
In this case, 1 receives two inputs and outputs one output, and the inside of the block has a multilayer perceptron configuration. The neural network schematic means first enters the pseudo-synaptic coupler 20a, and the pseudo-synaptic coupler 20a outputs the weight coupling coefficient ω
A product operation ωi · fi of i and the input signal fi is performed. All signals output from the pseudo-synaptic coupler 20a are added to the adder 2
0b, the addition processing y = Σωi · fi is performed, and the result is output to the non-linear converter 20c and becomes the final signal. Nonlinear converter 2
0 is composed of a nonlinear function called a sigmoid function,
A signal output from the adder 20b is input. The sigmoid function is
【0018】[0018]
【数1】 (Equation 1)
【0019】で定義される。次に流量入力手段の信号と
非線形変換器20cとの最終信号とから誤差演算手段2
0dで修正量、即ち誤差信号(最小2乗平均誤差法で評
価)を次式Is defined by Next, error calculation means 2 is calculated from the signal of the flow rate input means and the final signal of the non-linear converter 20c.
At 0d, the correction amount, that is, the error signal (evaluated by the least mean square error method) is expressed by the following equation.
【0020】[0020]
【数2】 (Equation 2)
【0021】より求める。修正手段20eでは誤差信号
ε(f)が最小となるように擬似シナプス結合器20a
の重み結合係数ωiの修正量を求める。修正量はCalculate from the following. In the correcting means 20e, the pseudo synapse coupler 20a is adjusted so that the error signal ε (f) is minimized.
The correction amount of the weight coupling coefficient ωi is obtained. The correction amount is
【0022】[0022]
【数3】 (Equation 3)
【0023】となる。以上の処理を繰り返して、流量Q
と流量検出手段19に出力信号fとの関係を示す式 Q
=δ・f (δは非線形特性を有する係数)の係数δを
学習し、流量入力手段20の出力信号Qとδ・fとの値
が一致するように調整している。## EQU1 ## By repeating the above processing, the flow rate Q
Showing the relationship between the output signal f and the output signal f
= Δ · f (δ is a coefficient having a non-linear characteristic) is learned, and the output signal Q of the flow rate input means 20 is adjusted so that the value of δ · f matches.
【0024】このようにして重み付け結合係数ωiを最
適化したのち、流量検出手段19の信号を入力し神経回
路網模式手段21でそのときの流量を求める。もとめた
流量を流量積算手段22で積算し、積算値を表示手段1
8で表示する。After optimizing the weighted coupling coefficient ωi in this way, the signal of the flow rate detecting means 19 is input, and the flow rate at that time is obtained by the neural network model means 21. The obtained flow rate is integrated by the flow integration means 22, and the integrated value is displayed by the display means 1.
Indicated by 8.
【0025】この実施例の構成によれば、流量と流量検
出手段19の検出信号との関係を示す係数を、神経回路
網模式手段21を用いることにより本来有する非線形特
性に推定できるので、高精度の流量演算ができ、その結
果積算流量などの流量計測を正確に行える。According to the configuration of this embodiment, the coefficient indicating the relationship between the flow rate and the detection signal of the flow rate detecting means 19 can be estimated to the inherent nonlinear characteristic by using the neural network model means 21, so that high accuracy is achieved. Flow rate calculation, and as a result, a flow rate measurement such as an integrated flow rate can be accurately performed.
【0026】本発明は流体流量を計測するフルイディッ
ク流量計の例をあげたが他の流量計に関しても上記の内
容を適用できる。Although the present invention has been described with respect to a fluidic flow meter for measuring a fluid flow rate, the above description can be applied to other flow meters.
【0027】[0027]
【発明の効果】以上説明したように本発明の流量計は、
流体流量を検出する流量検出手段と、予め検出して入力
された流量入力手段、流量入力手段と流量検出手段の出
力信号との非線形特性を示す関数を推定し流量値を求め
る神経回路網模式手段と、流量検出手段の出力信号と神
経回路網模式手段で推定された関数とからなり、流量入
力手段及び流量検出手段の出力信号によって神経回路網
模式手段で流量と検出信号との関係を示す関数を推定
し、即ち神経回路網模式手段の重み付け結合係数を学習
し関数を最適化することによって、その後流量検出手段
の信号をもとに推論を行い流量を求め、次に得た流量よ
り積算流量を演算し求めるので、線形近似した場合に比
べ誤差を極めて小さくでき、かつ流量演算が高精度に行
えるので積算流量などの流量計測が正確に出来るという
効果がある。As described above, the flow meter according to the present invention has the following features.
Flow rate detecting means for detecting a fluid flow rate, and a neural network schematic means for estimating a function indicating a non-linear characteristic of a flow rate input means, a flow rate inputting means and an output signal of the flow rate detecting means which are detected and input in advance and obtaining a flow rate value And a function indicating the relationship between the flow rate and the detection signal in the neural network model means by the output signals of the flow rate input means and the flow rate detecting means, the output signal of the flow rate detecting means and the function estimated by the neural network model means. , That is, by learning the weighted coupling coefficient of the neural network model means and optimizing the function, then inferring based on the signal of the flow rate detecting means to obtain the flow rate, and then calculating the integrated flow rate from the obtained flow rate Is calculated, the error can be made extremely small as compared with the case of linear approximation, and the flow rate calculation can be performed with high accuracy, so that there is an effect that the flow rate measurement such as the integrated flow rate can be accurately performed.
【図1】本発明の一実施例における流量計の制御ブロッ
ク図FIG. 1 is a control block diagram of a flow meter according to an embodiment of the present invention.
【図2】同流量計の制御装置に用いる神経回路網模式手
段のブロック図FIG. 2 is a block diagram of a neural network model used in the control device of the flow meter.
【図3】従来の流量計のシステム図FIG. 3 is a system diagram of a conventional flow meter.
【図4】同流量計の制御ブロック図FIG. 4 is a control block diagram of the flow meter.
【図5】同流量計の制御装置の特性図FIG. 5 is a characteristic diagram of a control device of the flow meter.
【図6】同流量計の制御装置の詳細特性図FIG. 6 is a detailed characteristic diagram of a control device of the flow meter.
19 流量検出手段 20 流量入力手段 21 神経回路網模式手段 19 flow detection means 20 flow input means 21 neural network schematic means
フロントページの続き (56)参考文献 特開 平3−44512(JP,A) 特開 平5−264317(JP,A) 特開 昭58−66820(JP,A) 特開 平3−274031(JP,A) 特開 昭63−210713(JP,A) (58)調査した分野(Int.Cl.7,DB名) G01F 1/00 G01F 1/20 G01F 15/075 Continuation of the front page (56) References JP-A-3-44512 (JP, A) JP-A-5-264317 (JP, A) JP-A-58-66820 (JP, A) JP-A-3-274403 (JP, A) , A) JP-A-63-210713 (JP, A) (58) Fields investigated (Int. Cl. 7 , DB name) G01F 1/00 G01F 1/20 G01F 15/075
Claims (1)
検出して入力された流量入力手段、前記流量入力手段と
前記流量検出手段の出力信号との非線形特性を示す関数
を推定し流量値を求める神経回路網模式手段とからなる
流量計。1. A flow rate detecting means for detecting a flow rate of a fluid, a flow rate input means previously detected and input, and a function indicating a nonlinear characteristic between an output signal of the flow rate input means and an output signal of the flow rate detecting means are estimated. Flowmeter comprising a neural network schematic means for determining
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP4066775A JP3036217B2 (en) | 1992-03-25 | 1992-03-25 | Flowmeter |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP4066775A JP3036217B2 (en) | 1992-03-25 | 1992-03-25 | Flowmeter |
Publications (2)
Publication Number | Publication Date |
---|---|
JPH05273004A JPH05273004A (en) | 1993-10-22 |
JP3036217B2 true JP3036217B2 (en) | 2000-04-24 |
Family
ID=13325580
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP4066775A Expired - Fee Related JP3036217B2 (en) | 1992-03-25 | 1992-03-25 | Flowmeter |
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JP (1) | JP3036217B2 (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
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CN101865710B (en) * | 2010-05-26 | 2012-01-04 | 北京航空航天大学 | Method for measuring flow of negative-pressure gas |
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1992
- 1992-03-25 JP JP4066775A patent/JP3036217B2/en not_active Expired - Fee Related
Also Published As
Publication number | Publication date |
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JPH05273004A (en) | 1993-10-22 |
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