CN101858811A - Method for compensating signal of high-precision pressure sensor - Google Patents
Method for compensating signal of high-precision pressure sensor Download PDFInfo
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- CN101858811A CN101858811A CN 201010202078 CN201010202078A CN101858811A CN 101858811 A CN101858811 A CN 101858811A CN 201010202078 CN201010202078 CN 201010202078 CN 201010202078 A CN201010202078 A CN 201010202078A CN 101858811 A CN101858811 A CN 101858811A
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Abstract
The invention discloses a method for compensating a signal of a high-precision pressure sensor. The method comprises the following steps of: inputting a pressure measuring signal and a temperature measuring signal measured by a pressure sensor into a digital signal processor; converting an original pressure signal into a pressure signal which is not subjected to temperature compensation and capable of eliminating lagging errors through a lagging error compensation method; carrying out temperature correction on the pressure signal through a signal interface processing method to acquire the pressure signal subjected to the temperature correction; and processing the pressure signal and the temperature signal subjected to the temperature correction to acquire a pressure signal and a temperature signal subjected to temperature compensation and non-linear error compensation through a temperature compensating method. The invention can compensate lagging errors and non-linear errors of the pressure sensor and errors caused by the change of the environment temperature, and can improve the measurement precision of the pressure sensor.
Description
Technical field:
The invention belongs to the signal Processing field, relate to a kind of method for compensating signal of sensor, the compensation of error method that especially a kind of nonlinearity erron for silicon pressure sensor, lag error and temperature variation cause.
Background technology:
Silicon pressure sensor is the most successful product sensor of micromechanical process, mainly contains three kinds of silicon piezoresistance type, condenser type and resonant modes, and wherein silicon piezoresistance type is most widely used general.Silicon piezoresistance type pressure sensor utilizes piezoresistive effect, resistance bridge principle, integrated circuit technology and the micromachining technology of Semiconducting Silicon Materials to make.Silicon piezoresistance type pressure sensor is because of advantages such as its microminiaturization, high sensitivity, response are fast, integrability and high stabilities, miniature vacuum meter, absolute manometer, velocimeter, flowmeter, sonic transducer, pneumatic process controller etc. are made in widespread use now, and its application spreads all over hard-core technology and industrial circles such as oil, chemical industry, biology, medical treatment, space flight, oceanographic engineering, atomic energy.
The static index of weighing sensor performance mainly contains nonlinearity erron, lag error and reproducibility error.In order to improve the measuring accuracy of sensor, need compensate these errors.At present, the compensation method of nonlinearity erron is very ripe, and the compensation method of nonlinearity erron commonly used has look-up table, curve fitting method and neural network method.Reproducibility error belongs to stochastic error, need analyze by statistical method, can't compensate it at present.Sluggishness is a kind of many-valued correspondence, unconventional, non-level and smooth phenomenon, and it is to be caused by energy absorption and transmission delay that the sensor internal element exists.Sluggishness is relevant with the loading procedure of the extraneous load that sensor is subjected to.Because the rule of lag error is very complicated, so also do not compensate the report of application at present about the lag error of silicon pressure sensor.The proportion that lag error accounts for fundamental error about 30%, is the key factor that influences the silicon pressure sensor measuring accuracy usually.
The shortcoming of silicon piezoresistance type pressure sensor is very responsive to temperature variation, and its output at zero point and sensitivity all can produce small variation along with temperature variation, and this phenomenon is called temperature drift.In order to reduce the influence of temperature variation, need the error that temperature variation causes be compensated the sensor measurement precision.Temperature compensation commonly used in the industry at present has: hardware compensating method and software compensation method (Computer Compensation, microprocessor compensation).The hardware compensating method has compensating circuit methods such as diode, triode, thermistor.Software compensation method is to utilize computing machine or microprocessor to gather pressure signal, temperature signal, adopts Digital Signal Processing that the error that temperature drift produces is compensated, and obtains high-precision pressure signal.
Summary of the invention:
The technical problem to be solved in the present invention is in order to overcome deficiency that lag error can't compensate in the existing silicon pressure sensor measurement and the measuring accuracy that improves silicon pressure sensor, provide a kind of and can compensate lag error, compensate the nonlinearity erron of silicon pressure sensor and the high-precision signal disposal route of the error that temperature variation produces simultaneously.
The technical solution used in the present invention is a kind of method for compensating signal of high-precision pressure sensor, described method is applied in the intelligent pressure sensor system of author's development, and this system comprises silicon pressure sensor, signal amplification circuit, analog to digital conversion circuit (A/D), DSP data acquisition compensating circuit, interface circuit and industrial control computer; Be connected with signal amplification circuit and analog to digital conversion circuit (A/D) on the described silicon pressure sensor respectively, signal amplification circuit is connected with analog to digital conversion circuit (A/D) again simultaneously; Be connected with DSP data acquisition compensating circuit on the analog-digital conversion circuit as described (A/D); Described DSP data acquisition compensating circuit is by interface circuit and industrial control computer; Described interface circuit comprises CAN fieldbus and USB interface; The workflow of described system: the temperature signal of sensor environment, pass through analog to digital conversion circuit (A/D) with the voltage signal that amplifies through signal, by analog signal conversion is digital signal, pass through digital signal processor (DSP) again and carry out digital signal processing, obtain the high-precision pressure signal and the temperature signal of lag error compensation, temperature compensation and nonlinear error compensation.At last, by CAN fieldbus or USB interface data transmission is arrived industrial control computer.
Method for compensating signal of high-precision pressure sensor, according to following steps:
(1) silicon pressure sensor measures pressure measurement signal Vp and temperature measurement signal Vt; Pressure measurement signal Vp enters DSP data acquisition compensating circuit successively behind signal amplification circuit and A/D change-over circuit; Temperature measurement signal Vt enters DSP data acquisition compensating circuit behind the A/D change-over circuit;
(2) in DSP data acquisition compensating circuit, adopt the lag error compensation method pressure measurement signal Vp to be converted into the pressure value P of eliminating lag error ';
(3) in DSP data acquisition compensating circuit, adopt the signaling interface disposal route to pressure value P ' carry out temperature correction, obtain through the pressure signal Vpm after the temperature correction;
(4) in DSP data acquisition compensating circuit, adopt temperature compensation, obtain the pressure signal P and the temperature signal T of process temperature compensation and nonlinear error compensation by pressure signal Vpm that passes through temperature correction and temperature signal Vt.
Described lag error compensation method is meant:
At first, with extreme value sequence Vp1, the Vp2 of pressure measurement signal Vp ..., Vpn represents pressure; Secondly, judge that pressure is in still uninstall process (be pressure load successively decrease process) of loading procedure (being the pressure load increasing process); Utilize sluggish inversion model then respectively
Or
To extreme value sequence Vp1, the Vp2 of pressure measurement signal Vp ..., Vpn handles, and obtains the pressure signal P through the lag error compensation ' sequence (P '
1, P '
2..., P '
n);
When pressure in loading procedure, be used for the sluggish inversion model of lag error compensation
For
Wherein, α
nFor through the current pressure value P of lag error compensation '
n, current pressure is in loading procedure;
Y is an input vector, by the current pairing voltage Δ of extreme value pressure increment V
nWith previous extreme value pressure P
N-1Form, i.e. Y=(Δ V
n, P
N-1), Δ V wherein
n=Vp
n-Vp
N-1
Y
iBe support vector, i.e. the vector that constitutes by training sample, i.e. Y
i=(x
i(a
i, b
i), b
i), (i=1,2 ..., 45); α
iFor through the weights coefficient of the support vector machine that obtains of training (i=1,2 ..., 45);
The sluggish inversion model that in uninstall process, is used for the lag error compensation when pressure
For
Wherein, b
nFor through the current pressure value P of lag error compensation '
n, current pressure is in uninstall process;
Y is an input vector, by the current pairing voltage Δ of extreme value pressure increment V
nWith previous extreme value pressure P
N-1Form, i.e. Y=(Δ V
n, P
N-1), Δ V wherein
n=Vp
n-Vp
N-1
Y
iBe support vector, i.e. the vector that constitutes by training sample, i.e. Y
i=(x
i(a
i, b
i), a
i), (i=1,2 ..., 45);
α
iFor through the weights coefficient of the support vector machine that obtains of training (i=1,2 ..., 45).
Described signal Processing interface method is meant: utilize pressure signal Vpm about the function model Vpm=f of pressure P and temperature T (P ', Vt), by not temperature compensated pressure signal P ' and temperature measurement signal Vt handle the pressure signal Vpm obtain through temperature correction; Function model Vpm=f (P ', Vt) be shown below:
Vpm＝-5.4969×10
-6+0.7526×P+0.8192·Vt+4.8869×10
-4·P
2-0.02361·P·Vt-0.03881·Vt
2。
Described temperature compensation is meant: utilize the function model P=g (Vpm of pressure P about pressure signal Vpm-temperature signal Vt, Vt) and temperature T about the function model T=q (Vpm of pressure signal Vpm-temperature signal Vt, Vt), pressure signal Vpm and temperature measurement signal Vt are treated to: through the high-precision pressure signal P and the temperature signal T of temperature compensation and nonlinear error compensation;
Pressure P about the function model P=g of pressure signal Vpm-temperature signal Vt (Vpm, Vt):
P＝-117.758+1.335×Vpm+45.134×Vt-0.00129×Vpm
2+0.0477×Vpm·Vt-4.5113×Vt
2
Temperature T about the function model T=q of pressure signal Vpm-temperature signal Vt (Vpm, Vt):
T＝2693.282-1.3888×Vpm-1182.152×Vt+0.00103×Vpm
2+0.2441×Vpm·Vt+130.1434×Vt
2。
The invention has the beneficial effects as follows: effectively compensated the lag error of silicon pressure sensor, compensated the nonlinearity erron of silicon pressure sensor and the error that temperature variation produces simultaneously, improved the measuring accuracy of silicon pressure sensor; This is a kind of digital signal processing method of brand-new silicon pressure sensor error compensation; The resultnat accuracy of process the inventive method compensation is that the error of the pressure transducer of 0.2%FS (range) can reduce half.
Description of drawings:
Fig. 1 is the resistance bridge synoptic diagram of silicon pressure sensor;
Fig. 2 is the intelligent pressure sensor system construction drawing that uses among the embodiment;
Fig. 3 is the structural drawing of method for compensating signal of high-precision pressure sensor of the present invention;
Fig. 4 is sluggish model x (a, experimental data drafting figure b) that the present invention uses;
Fig. 5 is the structure of the support vector machine used of the present invention;
Fig. 6 is the experimental data drafting figure of silicon pressure sensor about pressure P and temperature T;
Fig. 7 is the input pressure figure when temperature is 30 ℃ in the embodiment 1 lag error compensation experiment;
Fig. 8 is embodiment 1 experimental result: through sluggish compensation and uncompensated error amount comparison diagram;
Fig. 9 is the input pressure figure of the temperature during embodiment 2 lag errors compensation and temperature compensation are tested when being 65 ℃;
Figure 10 is embodiment 2 experimental results: through the present invention compensation and the error amount comparison diagram of nonlinear error compensation only.
Embodiment:
Below in conjunction with accompanying drawing the present invention is done and to describe in further detail:
In Fig. 1, four force sensing resistances of silicon pressure sensor constitute resistance bridge.In order to improve the measuring accuracy of sensor, silicon pressure sensor adopts constant current source power supply.Owing to adopt constant current source power supply, the variation of the constant current source voltage at electric bridge A, C two ends then reflects the variation of sensor place environment temperature, and the output voltage at electric bridge B, D two ends has reflected input pressure, and pressure transducer of this usefulness system of measuring pressure, temperature simultaneously is commonly called " bridge two is surveyed " system.Use " bridge two is surveyed " system in the present embodiment, can reduce the serviceability temperature sensor like this, make things convenient for on-the-spot test, save experimental cost.Certainly,, can not adopt " bridge two survey " scheme for method for compensating signal of high-precision pressure sensor of the present invention yet, temperature analog signal also can from be arranged on the same environment of silicon pressure sensor temperature sensor in obtain.
Fig. 2 is the structure of the intelligent pressure sensor system of the author development used among the embodiment.The intelligent pressure sensor system is made up of silicon piezoresistance type pressure sensor, signal amplification circuit, analog to digital conversion circuit, digital collection treatment circuit and industrial control computer.The constant current source voltage of silicon pressure sensor is as temperature signal, pass through analog to digital conversion circuit (A/D) with the voltage signal that amplifies through signal, by analog signal conversion is digital signal, pass through digital signal processor (DSP) again and carry out digital signal processing, obtain the high-precision pressure signal and the temperature signal of lag error compensation, temperature compensation and nonlinear error compensation.At last, by CAN fieldbus or USB interface data transmission is arrived industrial control computer.Wherein, DSP is responsible for the function of each chip operation, data acquisition, digital signal processing and communication as the core of total system.
The workflow of system is: after powering on, at first, the program initialization of system; Secondly, the DSP inquiry is sent acquisition by USB or CAN interface by industrial computer; If receive acquisition, then open a CPU timer, in interrupting, gathers by timer pressure signal and temperature signal, carry out digital filtering, software compensation then; At last, pressure, temperature data after the compensation are uploaded to industrial computer by USB or CAN interface; After the data upload, the finish command is gathered in the DSP inquiry, if do not receive collection the finish command, system continues to gather, processing signals; If receive collection the finish command, system finishing task.
Fig. 3 is the structure of method for compensating signal of high-precision pressure sensor, comprises the lag error compensation method, signal Processing interface method and temperature compensation.
The purpose of lag error compensation method is the lag error that pressure measurement signal Vp produces in the process of eliminating the pressure P loading, unloading.The lag error compensation method comprises two parts: first is the extreme value sequence of the pressure measurement signal Vp in the record pressure-loaded process, and this is because sluggish relevant with loading procedure, the extreme value sequential recording of pressure measurement signal Vp the pressure-loaded process.Second portion is to judge that at first pressure is in loading or uninstall process, utilizes sluggish inversion model then respectively
Or
Extreme value sequence to pressure measurement signal Vp is handled, and obtains the pressure signal P through the lag error compensation '.
Fig. 4 is sluggish model x (a, the drafting figure of experimental data b).Setting up correct, high-precision sluggish model and inversion model is the key of lag error compensation program.The sluggish model of silicon pressure sensor is on the pressure P-pressure measurement signal Vp calibration experiment data basis that is based upon about lag error.Concrete experimentation is as follows: 30 ℃ of room temperatures, under the humidity 56%RH condition, experimentize with reference to JB/T 10524-2005 machinery industry standard, experimental apparatus mainly contains: pressure sensor calibrating worktable, constant current source, temperature control box and high accuracy number multimeter.The range of the silicon pressure sensor of using among the embodiment is 40Mpa, take all factors into consideration training sample to the influence of fitting precision and the complicacy of testing experiment, 0～40Mpa is divided into 8 five equilibriums to be tested, load is loaded into 5Mpa from 0Mpa, write down output voltage, off-load is write down output voltage to 0Mpa then, calculate x (5,0).Reload 10Mpa, write down output voltage, off-load is write down output voltage to 5Mpa, and off-load is write down output voltage to 0Mpa again, calculate respectively x (10,5) and x (10,0), the rest may be inferred, up to 40Mpa, obtains output voltage x (a, b) experimental data between extreme value.(a, experimental data b) can obtain respectively about a through data processing sluggish model x, the sluggish inversion model of b
With
Modeling data.
By (a, experimental data b) and the modeling data of sluggish inversion model carry out regretional analysis, can obtain the sluggish model of silicon pressure sensor and be used for the inversion model that lag error compensates to sluggish model x.Regression analysis commonly used has methods such as quadric surface regression analysis, neural network.In order to improve the model accuracy that regretional analysis is set up, take into account modeling efficiency, the present invention adopts the method for support vector machine that modeling data is carried out regretional analysis.Support vector machine (Support VectorMachine is called for short SVM) is a kind of machine learning algorithm based on Statistical Learning Theory.It is to be based upon on Statistical Learning Theory and the structure risk minimum principle basis, between the complicacy of model and learning ability, seek optimal compromise according to limited sample information, in the hope of obtaining the machine learning algorithm of best popularization ability, can guarantee that resultant separating is that the overall situation is separated most.Support vector machine shows distinctive advantage in solving small sample, nonlinear problem.
Fig. 5 is the structure of the support vector machine used of the present invention, when the mathematical model of support vector machine is carried out data fitting to training sample, represents with following formula.
Wherein, Y is tested input vector;
N is the quantity of support vector, i.e. sample size;
Y
iBe support vector, i.e. the vector that constitutes by training sample.(i＝1，2，...，n)；
X (Y) is the output quantity corresponding with Y;
α
iFor with the corresponding Lagrange multiplier of weights coefficient.(i＝1，2，...，n)；
β is a threshold value;
K (x
i, x) be the kernel function of support vector machine.
Support vector machine has the kernel function of various ways, for example: linear kernel function, polynomial kernel function and kernel function such as basic kernel function radially.Support vector machine regression model of the present invention uses radially basic kernel function, and is less because radially basic kernel function is the deviation that produces.Radially basic kernel function is shown below
Wherein, || Y-Y
i|| expression input vector and support vector are asked mould after getting difference;
P is the kernel function parameter, adjusts the measuring accuracy that p can improve support vector machine.
Learning parameter: the selection of kernel function parameter p, insensitive loss function ε and penalty factor C has very big influence for the training effectiveness and the data fitting precision of support vector machine.In the practical application, determination method for parameter mainly contains experience and determines and grid search.The author finally selects parameter to be by sample repeatedly being trained and comparing:
Kernel function parameter p=5;
Insensitive loss function parameter ε=0.0001;
Penalty factor C=1000;
The learning sample that modeling data is constituted is as support vector, and once all input constitutes support vector machine, then each input vector in the training sample is imported support vector machine successively and trains; Based on training sample and the minimum principle of structure risk, solve the SVM structural parameters, make the deviation minimum of the desired output vector in output vector and the training sample, at this moment, the training of support vector machine finishes.Be met the structural parameters based on the support vector machine of training sample of error requirements at last: the weights factor alpha
1, α
2..., α
nAnd threshold value beta.
It is as follows that the support vector machine of employing said structure and parameter is set up the model that is used for the lag error compensation:
When pressure in loading procedure, be used for the sluggish inversion model of lag error compensation
For
Wherein, α
nFor through the current pressure value P of lag error compensation '
n, current pressure is in loading procedure;
Y is an input vector, by the current pairing voltage Δ of extreme value pressure increment V
nWith previous extreme value pressure P
N-1Form, i.e. Y=(Δ V
n, P
N-1), Δ V wherein
n=Vp
n-Vp
N-1
Y
iBe support vector, i.e. the vector that constitutes by training sample, i.e. Y
i=(x
i(a
i, b
i), b
i), (i=1,2 ..., 45);
α
iFor through the weights coefficient of the support vector machine that obtains of training (i=1,2 ..., 45), weights coefficient (α
1, α
2..., α
45)=
(-2.6512??-16.9909??-3.8090??62.4542??28.7184??8.5403??-121.7887-70.8702??-26.0235??-12.2707
182.5700??122.7173??64.4205??44.4448??33.8128??-193.8146??-147.9245-90.5564??-68.5294??-60.2854
-27.1669??189.9419??146.2461??112.2237??83.7839??86.4418??64.3222??48.3573-124.0973??-106.7623
-84.5529??-67.1388??-63.9511??-65.9918??-55.2276??-21.8993??72.0294??50.453152.5182??43.0396
40.8106??44.0773??48.0054??27.7502??38.5530)
Threshold value beta=0
The sluggish inversion model that in uninstall process, is used for the lag error compensation when pressure
For
Wherein, b
nFor through the current pressure value P of lag error compensation '
n, current pressure is in uninstall process;
Y is an input vector, by the current pairing voltage Δ of extreme value pressure increment V
nWith previous extreme value pressure P
N-1Form, i.e. Y=(Δ V
n, P
N-1), Δ V wherein
n=Vp
n-Vp
N-1
Y
iBe support vector, i.e. the vector that constitutes by training sample, i.e. Y
i=(x
i(a
i, b
i), a
i), (i=1,2 ..., 45);
α
iFor through the weights coefficient of the support vector machine that obtains of training (i=1,2 ..., 45), weights coefficient (α
1, α
2..., α
45)=
(-2.7459??-11.0461??12.1109??-6.2765??11.6702??-2.7377??-18.6998??35.3271-37.7082??22.8823
-5.8964??15.5362??-11.6501??7.9822??8.0876??-28.7951??59.5846-82.8821??81.8036??-53.0894
21.1136??-30.2184??88.5497??-124.8046??143.7415??-94.2631??36.5565??13.2669-74.1277??216.7344
-415.9337??528.2465??-564.6080??425.8700??-171.0906??43.4731??-18.934981.7430??-173.8384??239.6889
-208.0146??157.9626??-57.5958??8.9726??37.1454)
Threshold value beta=0
The purpose of signal Processing interface method is to connect lag error compensation method and temperature compensation, simultaneously the pressure signal that compensates through lag error but compensate without excess temperature is carried out temperature correction.Pressure signal Vpm in the signal Processing interface method about the function model Vpm=f of pressure P and temperature T (P ', Vt) be to be based upon on the pressure measurement signal Vp-temperature signal Vt calibration experiment data basis of silicon pressure sensor about pressure P and temperature T, obtain by regression analysis.
Fig. 6 is the experimental data drafting figure of silicon pressure sensor about pressure P and temperature T.The detailed process of experiment is as follows: experimental apparatus mainly contains: pressure sensor calibrating worktable, constant current source, temperature control box and high accuracy number multimeter experimentize with reference to JB/T 10524-2005 machinery industry standard.The range of the silicon piezoresistance type pressure sensor of using among the embodiment is 40Mpa, silicon piezoresistance type pressure sensor is packed in the temperature control box, be respectively the pressure-BD terminal voltage-measurement of AC terminal voltage, the record that carry out pressure transducer under 20 ℃, 30 ℃, 40 ℃, 50 ℃, 60 ℃, 65 ℃ the condition in temperature.The pressure range is 0～40Mpa, locates the recording voltage output valve at 0Mpa, 5Mpa, 10Mpa, 15Mpa, 20Mpa, 25Mpa, 30Mpa, 35Mpa, these 9 of 40Mpa.The loading procedure of pressure transducer is for to be loaded into full scale 40Mpa gradually from 0Mpa, and then is decremented to 0Mpa gradually from full scale.At last, obtain experimental data: pressure P-temperature T-pressure measurement signal Vp-temperature signal Vt.Because sluggish existence, thus when uniform temp, uniform pressure the pressure measurement signal Vp difference of positive and negative stroke.Therefore, will be when uniform temp, uniform pressure positive and negative stroke pressure signal Vp average, obtain pressure signal Vpm, pressure signal Vpm and pressure P are a kind of mapping relations one by one.
From the experimental data of silicon pressure sensor, obtain modeling data: pressure signal Vpm-pressure signal P-temperature signal Vt about pressure P and temperature T, by these data are carried out regretional analysis, obtain pressure signal Vpm about the function model Vpm=f of pressure signal P and temperature signal Vt (P ', Vt).Use among the present invention quadric surface regretional analysis build-up pressure signal Vpm about the function model Vpm=f of pressure signal P and temperature signal Vt (P ', Vt), function model is shown below:
Vpm＝-5.4969×10
-6+0.7526×P+0.8192·Vt+4.8869×10
-4·P
2-0.02361·P·Vt-0.03881·Vt
2
Utilize the function model of pressure signal Vpm, by not temperature compensated pressure value P about pressure signal P and temperature signal Vt ' and temperature signal Vt handle the pressure signal Vpm obtain through temperature correction.
Described temperature compensation is: on the pressure measurement signal Vp-temperature signal Vt calibration experiment data basis of silicon pressure sensor about pressure P and temperature T, by the function model P=g (Vpm of quadric surface regression analysis build-up pressure P about pressure signal Vpm-temperature signal Vt, Vt) and temperature T about the function model T=q of pressure signal Vpm-temperature signal Vt (Vpm, Vt); (Vpm, Vt) (Vpm Vt), is treated to pressure signal Vpm and temperature signal Vt: through the pressure signal P and the temperature signal T of temperature compensation and nonlinear error compensation with temperature T function model T=q to utilize pressure P function model P=g.
Temperature compensation need working pressure P function model P=g (Vpm, Vt) and temperature T function model T=q (Vpm, Vt).These function models all are to be based upon on the pressure measurement signal Vp-temperature signal Vt calibration experiment data basis of silicon pressure sensor about pressure P and temperature T, and this experiment is identical about the pressure measurement signal Vp-temperature signal Vt calibration experiment of pressure P and temperature T with the silicon pressure sensor in the signal Processing interface method.From experimental data: pressure P-temperature T-pressure measurement signal Vp-temperature signal Vt, can obtain modeling data respectively: (pressure P-pressure signal Vpm-temperature signal Vt) and (temperature T-pressure measurement signal Vp-temperature signal Vt).These experimental datas are carried out the quadric surface regression analysis, build-up pressure P is about the function model P=g (Vpm of pressure signal Vpm-temperature signal Vt respectively, Vt) and temperature T (Vpm, Vt), function model is shown below about the function model T=q of pressure signal Vpm-temperature signal Vt.
Pressure P about the function model P=g of pressure signal Vpm-temperature signal Vt (Vpm, Vt):
P＝-117.758+1.335×Vpm+45.134×Vt-0.00128×Vpm
2+0.0477×Vpm·Vt-4.5113×Vt
2
Temperature T about the function model T=q of pressure signal Vpm-temperature signal Vt (Vpm, Vt):
T＝2693.282-1.3888×Vpm-1182.152×Vt+0.00103×Vpm
2+0.2441×Vpm·Vt+130.1434×Vt
2
(Vpm, Vt) (Vpm Vt), is treated to pressure signal Vpm and temperature signal Vt: through the pressure signal P and the temperature signal T of temperature compensation and nonlinear error compensation with temperature T function model T=q to utilize pressure P function model P=g.
Be two embodiment that do in order to check method for compensating signal of high-precision pressure sensor of the present invention below.Embodiment 1: the lag error compensation experiment is check lag error compensation effect and the experiment done under temperature-resistant condition.Embodiment 2: lag error compensation and temperature compensation experiment are the experiments of doing under the condition of temperature change.Experimental apparatus mainly contains: piston gage, constant-current supply, temperature-controlled cabinet and high accuracy number multimeter.The range of the silicon pressure sensor that uses among the embodiment is 0～40Mpa.26 ℃ of room temperatures, under the humidity 56%RH condition, test with reference to JB/T 10524-2005 machinery industry standard.
Embodiment 1: the lag error compensation experiment
In order to check the sluggish compensation effect of method for compensating signal of high-precision pressure sensor of the present invention, employing pressure extreme value sequence as shown in Figure 7 is as input pressure, and the temperature of sensor place temperature control box is 30 ℃.Output voltage as input signal, is calculated and compares with method for compensating signal of high-precision pressure sensor of the present invention with without sluggish compensation method respectively, and error ratio more as shown in Figure 8.Wherein, solid line is the error of the inventive method, and dotted line is the error of non-sluggish compensation method.The error amount analysis is more as shown in table 1.
Table 1
| Error ratio | AME | Mean square of error is taken root in |
| Through the lag error compensation | ??-0.0247 | ??0.0675 |
| Compensate without lag error | ??0.0505 | ??0.4252 |
By comparison of test results as can be known, the error of the force value after the sluggishness compensation is significantly less than the force value error without the sluggishness compensation.Therefore, for the lag error of silicon pressure sensor, it is effective using method for compensating signal of high-precision pressure sensor of the present invention.
Embodiment 2: lag error compensation and temperature compensation experiment
In order to check the compensation effect of method for compensating signal of high-precision pressure sensor integral body of the present invention, employing pressure as shown in Figure 9 is as input pressure, and the temperature of sensor place temperature control box is 65 ℃.Output voltage as input signal, is calculated with method for compensating signal of high-precision pressure sensor of the present invention and nonlinear error compensation method respectively and compares, and error ratio more as shown in figure 10.Wherein, solid line is the error of the inventive method, and dotted line is the error of nonlinear error compensation method.The error amount analysis is more as shown in table 2.
Table 2
| Error ratio | AME | Mean square of error is taken root in |
| Through sluggishness, nonlinear error compensation and temperature compensation | ??-0.1006 | ??0.0739 |
| Through nonlinear error compensation | ??-0.1396 | ??0.4744 |
As seen from the experiment, the error of the force value after sluggishness, nonlinear error compensation and temperature compensation is significantly reduced, and method for compensating signal of high-precision pressure sensor of the present invention is effective.
Above content is to further describing that the present invention did in conjunction with concrete preferred implementation; can not assert that the specific embodiment of the present invention only limits to this; for the general technical staff of the technical field of the invention; without departing from the inventive concept of the premise; can also make some simple deduction or replace, all should be considered as belonging to the present invention and determine scope of patent protection by claims of being submitted to.
Claims (4)
1. a method for compensating signal of high-precision pressure sensor is characterized in that, according to following steps:
(1) silicon pressure sensor measures pressure measurement signal Vp and temperature measurement signal Vt; Pressure measurement signal Vp enters DSP data acquisition compensating circuit successively behind signal amplification circuit and A/D change-over circuit; Temperature measurement signal Vt enters DSP data acquisition compensating circuit behind the A/D change-over circuit;
(2) in DSP data acquisition compensating circuit, adopt the lag error compensation method pressure measurement signal Vp to be converted into the pressure value P of eliminating lag error ';
(3) in DSP data acquisition compensating circuit, adopt the signaling interface disposal route to pressure value P ' carry out temperature correction, obtain through the pressure signal Vpm after the temperature correction;
(4) in DSP data acquisition compensating circuit, adopt temperature compensation, obtain the pressure signal P and the temperature signal T of process temperature compensation and nonlinear error compensation by pressure signal Vpm that passes through temperature correction and temperature signal Vt.
2. method for compensating signal of high-precision pressure sensor according to claim 1 is characterized in that described lag error compensation method is meant:
At first, with extreme value sequence Vp1, the Vp2 of pressure measurement signal Vp ..., Vpn represents pressure; Secondly, judge that pressure is in loading procedure or uninstall process; Utilize sluggish inversion model then respectively
Or
To extreme value sequence Vp1, the Vp2 of pressure measurement signal Vp ..., Vpn handles, and obtains the pressure signal P through the lag error compensation ' sequence P '
1, P '
2..., P '
n
When pressure in loading procedure, be used for the sluggish inversion model of lag error compensation
For
Wherein, a
nFor through the current pressure value P of lag error compensation '
n, current pressure is in loading procedure;
Y is an input vector, by the current pairing voltage Δ of extreme value pressure increment V
nWith previous extreme value pressure P
N-1Form, i.e. Y=(Δ V
n, P
N-1), Δ V wherein
n=Vp
n-Vp
N-1
Y
iBe support vector, i.e. the vector that constitutes by training sample, i.e. Y
i=(x
i(a
i, b
i), b
i), (i=1,2 ..., 45); α
iFor process is trained the weights coefficient i=1 of the support vector machine that obtains, 2 ..., 45;
The sluggish inversion model that is used for the lag error compensation when pressure in uninstall process is
Wherein, b
nFor through the current pressure value P of lag error compensation '
n, current pressure is in uninstall process;
Y is an input vector, by the current pairing voltage Δ of extreme value pressure increment V
nWith previous extreme value pressure P
N-1Form, i.e. Y=(Δ V
n, P
N-1), Δ V wherein
n=Vp
n-Vp
N-1
Y
iBe support vector, i.e. the vector that constitutes by training sample, i.e. Y
i=(x
i(a
i, b
i), a
i), i=1,2 ..., 45; α
iFor process is trained the weights coefficient i=1 of the support vector machine that obtains, 2 ..., 45.
3. method for compensating signal of high-precision pressure sensor according to claim 1, it is characterized in that, described signal Processing interface method is meant: utilize pressure signal Vpm about the function model Vpm=f of pressure P and temperature T (P ', Vt), by not temperature compensated pressure signal P ' and temperature measurement signal Vt handle the pressure signal Vpm obtain through temperature correction; Function model Vpm=f (P ', Vt) be shown below:
Vpm＝-5.4969×10
-6+0.7526×P+0.8192·Vt+4.8869×10
-4·P
2-0.02361·P·Vt-0.03881·Vt
2。
4. method for compensating signal of high-precision pressure sensor according to claim 1, it is characterized in that, described temperature compensation is meant: utilize the function model P=g (Vpm of pressure P about pressure signal Vpm-temperature signal Vt, Vt) and temperature T about the function model T=q (Vpm of pressure signal Vpm-temperature signal Vt, Vt), pressure signal Vpm and temperature measurement signal Vt are treated to: through the high-precision pressure signal P and the temperature signal T of temperature compensation and nonlinear error compensation;
Pressure P about the function model P=g of pressure signal Vpm-temperature signal Vt (Vpm, Vt):
P＝-117.758+1.335×Vpm+45.134×Vt-0.00128×Vpm
2+0.0477×Vpm·Vt-4.5113×Vt
2
Temperature T about the function model T=q of pressure signal Vpm-temperature signal Vt (Vpm, Vt):
T＝2693.282-1.3888×Vpm-1182.152×Vt+0.00103×Vpm
2+0.2441×Vpm·Vt+130.1434×Vt
2。
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