CN107024297A - A kind of sensor compensator of thermocouple - Google Patents
A kind of sensor compensator of thermocouple Download PDFInfo
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- CN107024297A CN107024297A CN201710280922.6A CN201710280922A CN107024297A CN 107024297 A CN107024297 A CN 107024297A CN 201710280922 A CN201710280922 A CN 201710280922A CN 107024297 A CN107024297 A CN 107024297A
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- sensor
- thermocouple
- compensator
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- chebyshev
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K7/00—Measuring temperature based on the use of electric or magnetic elements directly sensitive to heat ; Power supply therefor, e.g. using thermoelectric elements
- G01K7/02—Measuring temperature based on the use of electric or magnetic elements directly sensitive to heat ; Power supply therefor, e.g. using thermoelectric elements using thermoelectric elements, e.g. thermocouples
- G01K7/14—Arrangements for modifying the output characteristic, e.g. linearising
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- Measuring Temperature Or Quantity Of Heat (AREA)
Abstract
The invention discloses a kind of sensor compensator of thermocouple, including data sampling module, data processing module, telegon, wireless transmitter module, power module and monitoring center, the data collecting module collected is to parameters information and is transferred in data processing module.The sensor compensator of thermocouple of the present invention makes the system linearization that sensor is constituted with neural network module, the nonlinear characteristic of sensor is compensated, system after compensation can be handled by linear characteristic, not only increase accuracy in computation, and entirely calculate and realized by hardware circuit, real-time is good, has expanded the application of thermocouple sensor.
Description
Technical field
The present invention relates to a kind of sensor technology, specifically a kind of sensor compensator of thermocouple.
Background technology
In various instrument, temperature sensor is used as through conventional thermocouple.Temperature inversion can be by thermocouple can be direct
The electric signal of measurement, its feature is that measurement temperature scope is wide, and Measurement reliability is high, and itself can produce voltage, it is not necessary to additional
Driving power supply, it is easy to use.But the thermoelectrical potential of thermocouple is nonlinear with temperature relation, should be carried out in practical application non-linear
Compensation.It is in the past main to be handled using methods such as least square methods, but this method is comparatively laborious, it sometimes appear that solution side
Ill-condition matrix situation is run into during journey, and when environmental condition changes, the characteristic of sensor changes what need to be re-scaled
Shortcoming.Therefore, set forth herein set up nonlinear compensation model using orthogonal basis polynomials algebra neural networks and use corresponding god
Method through the mixed-media network modules mixed-media SN9701 thermocouple sensor nonlinear compensations realized.
The content of the invention
It is an object of the invention to provide a kind of sensor compensator of thermocouple, to solve to propose in above-mentioned background technology
The problem of.
To achieve the above object, the present invention provides following technical scheme：
A kind of sensor compensator of thermocouple, comprising thermocouple sensor and non-linear compensator, the thermocouple is passed
Sensor and non-linear compensator are connected in series.
A kind of sensor compensation method of thermocouple, is comprised the steps of：1st, temperature survey is carried out with thermocouple sensor,
Measurement result temperature is t, and the resistance value for reading the now thermocouple is d, then d=f (t)；2nd, connected after thermocouple sensor
One non-linear compensator, makes y=g (d)=kt.
It is used as the further technical scheme of the present invention：Y=g (d) expansion is g (d)=w0+w1T1+w2T2+…+wnT3
+ ..., wherein T1、T2、T3For the orthogonal fundamental polynoml of Chebyshev, tried to achieve by below equation：Tn(d)=2dTn-1(d)-Tn-2(d)
Wherein, (T0=1, T1=d, n=2,3,4 ...).
Compared with prior art, the beneficial effects of the invention are as follows：The sensor compensator of thermocouple of the present invention makes sensor
The system linearization constituted with neural network module, the nonlinear characteristic of sensor is compensated, and the system after compensation can be by line
Property characteristic processing, not only increase accuracy in computation, and entirely calculate and realized by hardware circuit, real-time is good, has expanded heat
The application of thermocouple sensor.
Brief description of the drawings
Fig. 1 is the overall block diagram of the sensor compensator of thermocouple.
Fig. 2 is Chebyshev neural network structure rough schematic view.
Fig. 3 is sensor input after compensation and output relation curve.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Refer to Fig. 1-3, a kind of sensor compensator of thermocouple, comprising thermocouple sensor and non-linear compensator,
The thermocouple sensor and non-linear compensator are connected in series.
A kind of sensor compensation method of thermocouple, is comprised the steps of：1st, temperature survey is carried out with thermocouple sensor,
Measurement result temperature is t, and the resistance value for reading the now thermocouple is d, then d=f (t)；2nd, connected after thermocouple sensor
One non-linear compensator, makes y=g (d)=kt.
Y=g (d) expansion is g (d)=w0+w1T1+w2T2+…+wnT3+ ..., wherein T1、T2、T3For Chebyshev just
Fundamental polynoml is handed over, is tried to achieve by below equation：Tn(d)=2dTn-1(d)-Tn-2Wherein, (d) (T0=1, T1=d, n=2,3,4 ...).
The present invention operation principle be：Thermocouple sensor nonlinear compensation principle is based primarily upon the basic ring shown in Fig. 1
Section.If the input of sensor is t, d is output as, d=f (t) is non-linear relation.If connecting a compensation ring after sensor
Section, makes y=g (d)=kt, then being achieved that the nonlinear compensation of sensor, as k=1, y=t=g (d) is referred to as sensor
Inversion model.
It is well known that three layers of BP neural network in theory can be with Approximation of Arbitrary Nonlinear Function, precision is also high, and it is learned
Habit process is by constantly adjusting network connection coefficient.But, the BP complicated network structures, it is impossible to provide solution to model analysis expression
Formula.For this, we are launched into nonlinear function y=g (d):
G (d)=w0+w1T1+w2T2+…+wnT3+…(1)
T in formula (1)1、T2、、T3For the orthogonal fundamental polynoml of Chebyshev, it can be tried to achieve by following recurrence formula:
Tn(d)=2dTn-1(d)-Tn-2Wherein, (d) (T0=1, T1=d, n=2,3,4 ...).
If known some groups of di,ti(i=1,2 ... m) value, that is, it can use the polynomial preceding n of the orthogonal fundamental polynoml of Chebyshev
The nonlinear compensation model of item approximate representation sensor, is designated asThe key of present problem is how to try to achieve coefficient w0、w1┅wn。
Because Chebyshev's orthogonal basis polynomials algebra neural networks can use single layer network for approaching arbitrary nonlinear function
" supervision " study is realized, and pace of learning is fast, by the study to demarcating sample value, automatically obtains these coefficients, network
Structure simplify and learning rules are simple, so, we set up thermocouple biography with Chebyshev's orthogonal basis polynomials algebra neural networks
The nonlinear compensation model of sensor, and the method for realizing with Chebyshev neural network module nonlinear compensation.
The modeling of nonlinear compensation link：
Fig. 2 show w in Chebyshev's orthogonal basis polynomials algebra neural networks structure rough schematic view, figurej(j=0,1,2 ...
N) it is the connection weight of network, it is T that it, which is inputted,0i=1, ui=T1i,(wherein:I is sample sequence number),
It is output as:
Weight w abovejIt can be adjusted using following algorithm:
wj(k)=wj(k+1)+η·ei(k)·Tji(4)
Wherein, yi(k),e(k),wj(k) desired output, estimation output, error and weights are represented respectively, and η represents steady
Qualitative and constringent learning coefficient.
According to (2), (3), (4) formula order, with sample value constantly in turn adjustment network connection weigh, until in a certain ratation school
In habit, learning objective target function:
A sufficiently small value is reached, connection weight w is obtained0…wn.(5) in formula, m is sample length.
Fig. 2 Chebyshev neural network structure rough schematic views：
By taking S types thermocouple (10%/platinum of platinum rhodium) as an example, if tested value tiThermoelectrical potential E is corresponded to respectivelyi(t), 800 DEG C~
Its phasing meter is as shown in table 1 in the range of 1400 DEG C.In order that the output V of thermocouple temperature transmitter0With the linear passes of temperature t
System, i.e. V01=ht, (t:℃,V0:V) the range of linearity determine within 800 DEG C~1400 DEG C, it is assumed that this temperature transmitter it is defeated
Go out for 0~5V, linear scale division value theoretical value V can be obtained01=(5/1400) × t respective value is as shown in table 1.
Using neural network algorithm presented hereinbefore, n=2 is taken, using formula (2), formula (3) and formula (4) to neutral net
Weight wj(j=0,1,2,3) is trained, the random number between the initial value optional (- 1,1) of weights.Because Chebyshev's nerve net
There is x inside networknForm link, Chebyshev polynomials TnLink is formed, so can be by 13 output thermoelectricity of sensor in table 1
Gesture value amplifies the input value as network, linear scale division value value V after 50 times01(consider to use Qie Bixue as the output valve of network
Husband's neural network module is realized.Sample value is sequentially inputted into neutral net successively, weights repaiied with study alternative manner
Just, through multiple learning process, until (5) formula reaches sufficiently small value, now learning process terminates, and corresponding weights are non-linear
The corresponding coefficient (n=2) of function.Neural metwork training program is write, the program is run, obtaining final result is：
w0=-0.1553w1=7.1915w2=-0.5123
Then the nonlinear compensation model analyzing formula of the thermocouple sensor is：
In formula (6), T0=1, T1=V1,T2=2V1 2-V1,T3=4V1 3-3V1。
Claims (3)
1. a kind of sensor compensator of thermocouple, it is characterised in that described comprising thermocouple sensor and non-linear compensator
Thermocouple sensor and non-linear compensator are connected in series.
2. a kind of sensor compensation method of thermocouple, it is characterised in that comprise the steps of：1st, carried out with thermocouple sensor
Temperature survey, measurement result temperature is t, and the resistance value for reading the now thermocouple is d, then d=f (t)；2nd, sensed in thermocouple
One non-linear compensator of series connection, makes y=g (d)=kt after device.
3. a kind of sensor compensation method of thermocouple according to claim 2, it is characterised in that y=g (d) expansion
Formula is, wherein T1、T2、T3For the orthogonal fundamental polynoml of Chebyshev, by following
Formula is tried to achieve：Tn(d)=2dTn-1(d)-Tn-2Wherein, (d) (T0=1, T1=d, n=2,3,4 …）.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102782468A (en) * | 2010-03-31 | 2012-11-14 | 密克罗奇普技术公司 | Thermocouple electromotive force voltage to temperature converter with integrated cold-junction compensation and linearization |
| CN203011562U (en) * | 2012-11-27 | 2013-06-19 | 大连海事大学 | Thermocouple signal acquisition and process apparatus based on STC12C5A60S2 single chip microcomputer |
| CN105466587A (en) * | 2014-08-15 | 2016-04-06 | 郭洪 | Multichannel temperature acquisition system based on K-type thermocouple and MAX6675s |
| CN105651409A (en) * | 2016-04-06 | 2016-06-08 | 中国南方航空工业(集团)有限公司 | Thermocouple cold junction compensation and temperature measurement circuit and device |
-
2017
- 2017-04-25 CN CN201710280922.6A patent/CN107024297A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102782468A (en) * | 2010-03-31 | 2012-11-14 | 密克罗奇普技术公司 | Thermocouple electromotive force voltage to temperature converter with integrated cold-junction compensation and linearization |
| CN203011562U (en) * | 2012-11-27 | 2013-06-19 | 大连海事大学 | Thermocouple signal acquisition and process apparatus based on STC12C5A60S2 single chip microcomputer |
| CN105466587A (en) * | 2014-08-15 | 2016-04-06 | 郭洪 | Multichannel temperature acquisition system based on K-type thermocouple and MAX6675s |
| CN105651409A (en) * | 2016-04-06 | 2016-06-08 | 中国南方航空工业(集团)有限公司 | Thermocouple cold junction compensation and temperature measurement circuit and device |
Non-Patent Citations (1)
| Title |
|---|
| 俞阿龙: "Chebyshew神经网络模块在铂电阻温度传感器非线性补偿中的应用", 《电气自动化》 * |
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Application publication date: 20170808 |