CN114487971B - Improved micro impedance measurement self-calibration algorithm and device - Google Patents
Improved micro impedance measurement self-calibration algorithm and device Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
- G01R35/005—Calibrating; Standards or reference devices, e.g. voltage or resistance standards, "golden" references
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- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
- G01N27/041—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
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Abstract
The invention relates to the technical field of structural health monitoring by a piezoelectric impedance method, and provides an improved micro impedance measurement self-calibration algorithm and device. And selecting the calibration resistor according to the corrected four groups of data amplitude values and the most stable point of the shape deviation, thereby realizing the self-calibration function of the miniature impedance measuring device. The data selected by the invention is the measurement data with the best calibration effect under the condition of comprehensively considering the precision of the impedance amplitude and the precision of the impedance phase.
Description
Technical Field
The invention relates to the technical field of structural health monitoring by a piezoelectric impedance method, in particular to an improved micro impedance measurement self-calibration algorithm and device.
Background
The piezoelectric impedance technology monitors the safety of the structure and the change of damage state information by detecting the change of impedance signals of a measured object, has higher sensitivity for monitoring early tiny damage of the structure, has the advantages of simple implementation, application to complex structures, low price of required sensors, light weight, small volume, high conversion efficiency, good long-term stability and the like. Therefore, the piezoelectric impedance technology is considered to be one of the most promising structural nondestructive testing technologies in the field of structural health monitoring.
Conventional impedance-based damage detection techniques mainly use a precision impedance analyzer for impedance measurements. However, the conventional piezoelectric impedance measuring device, i.e., the precision impedance analyzer, has the disadvantages of high price, large volume, inconvenience in carrying and measurement, and the like, so that domestic and foreign scholars begin to research and use economical, portable and small-sized miniature impedance measuring devices to replace the precision impedance analyzer for impedance detection.
The miniature impedance measuring equipment needs to be calibrated before being used, and the measurement result and the measurement effect in subsequent measurement can be seriously influenced by the quality of the calibration result. The former scholars mainly use a gain coefficient calibration method to realize independent calibration and impedance measurement of a miniature impedance measurement device. The gain coefficient calibration method mainly comprises the steps of storing different gain coefficients for different impedance measurement ranges in a single chip microcomputer in the miniature impedance measurement equipment and measuring an object to be measured by using different feedback resistors, so that the miniature impedance measurement equipment can be independently calibrated in actual use. For example, in patent document CN108562795A entitled solar impedance measurement system, the impedance measurement system developed by sudu, semiconductor research institute of university of chinese academy of sciences, the impedance measurement system adjusts the impedance measurement range according to the measurement result of the impedance measurement module and uses different feedback resistance values and gain coefficients, so that the measurement result is always kept within the range; for example, in patent document CN206002605U with patent name of a portable impedance measuring instrument based on STM32F105RC, the portable impedance measuring instrument developed by tangmingyu of the university of anhui physics automatically switches to an appropriate feedback resistor to measure impedance according to the range of a measuring signal based on an STM32 single chip microcomputer, and calculates an impedance value according to the measured real part and imaginary part of the impedance and fitting a fitting function; for example, in patent document CN213780212U entitled STM 32-based three-terminal device impedance measuring instrument, a three-terminal device impedance measuring instrument developed by zhao dynasty, ltd, li de instruments, selects and switches different feedback resistors according to the range of the measured impedance signal to measure the impedance, and calculates the final impedance value by using the gain factor obtained in the calibration mode.
The gain coefficient calibration method can ensure that the output voltage is controlled within the input range of a subsequent analog-digital converter, errors caused by overlarge or undersize response signals are avoided, but only the range of the impedance amplitude is considered and the influence of the impedance phase is not considered when range selection is carried out, the measured data can not be ensured to be closest to the accurate data in terms of the impedance amplitude and the curve shape, and the phenomenon of data record overflow is easy to occur, so that the measured curve is discontinuous, and the measurement result is not ideal.
In view of the above, overcoming the drawbacks of the prior art is an urgent problem in the art.
Disclosure of Invention
The invention aims to solve the technical problems that in the prior art, only the range of impedance amplitude is considered and the influence of impedance phase is not considered when range selection is carried out, the measured data can not be ensured to be closest to accurate data in consideration of the impedance amplitude and the curve shape, and the phenomenon of data record overflow is easy to occur, so that the measured curve is discontinuous, and the measurement result is not ideal.
The invention adopts the following technical scheme:
in a first aspect, the present invention provides an improved miniature impedance measurement self-calibration algorithm, including:
correcting the measured data of the resistor to be measured according to an impedance correction formula, and arranging all the corrected data from small to large according to the size of the used feedback resistance value;
respectively calculating the change rate of the amplitude relative error of every two adjacent groups of corrected measurement data along with the change rate of the feedback resistance value and the change rate of the curve shape relative error along with the change rate of the feedback resistance value;
selecting four resistors with the minimum amplitude relative error change rate along with the change rate of the feedback resistance value to form a candidate resistor interval RI1, and calculating a first score of each resistor according to the relative magnitude of the amplitude relative error change rate along with the change rate of the resistance value; selecting four resistors with the smallest change rate of the curve shape relative error along with the resistance value to form a candidate resistor interval RI2, and calculating a second score of each resistor according to the relative size of the curve shape relative error along with the change rate of the resistance value;
combining the selected candidate resistor section RI1 and the candidate resistor section RI2 to form a candidate resistor section RI, and summing a first score of each resistor in the candidate resistor section RI under the amplitude relative error change rate and a second score under the curve shape relative error change rate;
and selecting the resistor with the highest score after summation as the optimal feedback resistor.
Preferably, it is determined whether the selected optimal feedback resistance is unique, and if the summed resistances with the highest score are not unique, the method further includes:
and selecting the resistor with the corresponding resistance value closest to the resistance value at the center position of the candidate resistor section RI from the plurality of optimal feedback resistors.
Preferably, the correcting the measurement data according to an impedance correction formula includes a calibration stage and a measurement stage, specifically:
in the calibration phase, the feedback resistor R is connectedFBConnecting calibration resistors R with equal resistance valuescalAcquiring a first real part value R of the obtained calibration resistorSchoolAnd a first imaginary value ISchoolObtaining the modulus M of the calibration resistor according to the calculationSchoolCalculating the gain factor GF and the system phase thetasystem；
In the measuring stage, the calibration resistor is replaced by an object to be measured, and then a returned second real part value R is acquiredMeasuringAnd a second imaginary value IMeasuringCalculating to obtain the amplitude M of the object to be measuredMeasuringUsing gain factor GF versus impedance modulus MMeasuringMaking corrections and using the system phase θsystemAnd correcting the impedance phase of the object to be measured.
Preferably, the gain factor GF and the system phase θ are calculatedsystemThe calculation formula is as follows:
GF=(1/Rcal)/Mschool；
θsystem=arctan(ISchool/RSchool)×180°/π；
RcalTo calibrate the resistance value of the resistor, a known value;
the impedance modulus value of the calibration resistor measured in the calibration stage; i isSchoolFor the imaginary part of the impedance, R, of the calibration resistance measured during the calibration phaseSchoolThe real part of the impedance of the calibration resistor measured in the calibration stage.
Preferably, the gain factor GF is used to match the impedance modulus MSide surveyAnd correcting to obtain a corrected impedance amplitude Z, wherein the specific correction formula is as follows:
Z=(1/GF)/Mmeasuring=(MSchool×Rcal)MSide survey；
Wherein, the first and the second end of the pipe are connected with each other,
the impedance module value of the object to be measured is measured in the measuring stage; z is the impedance modulus M using the gain factor GFMeasuringCorrecting the impedance amplitude of the object to be detected; i isMeasuringThe impedance imaginary part of the object to be measured is measured in the measuring stage; rSide surveyThe real impedance part of the object to be measured in the measuring stage.
Preferably, the impedance phase θ is calculated according to the real impedance part of the object to be measured and the imaginary impedance part of the object to be measured in the measuring stageMeasuringComprises the following steps:
θside survey=arctan(IMeasuring/RMeasuring)×(180°/π)；
And using the system phase thetasystemImpedance phase theta of object to be measuredMeasuringAnd correcting by using a system correction formula as follows: θ = θMeasuring-θsystem(ii) a Where θ is the precise impedance phase.
Preferably, since the range of the arctan function is [ - π/2, π/2]And the range of the actual phase is [ -pi, pi]And thus the measured phase thetasystemAnd thetaMeasuringQuadrant correction of the phase is required; the quadrant correction comprises:
first quadrant, θx=arctan(Ii/Ri)×(180°/π)；
Second quadrant, θx=arctan(Ii/Ri)×(180°/π)+180°；
Third quadrant, θx=arctan(Ii/Ri)×(180°/π)+180°；
Fourth quadrant, θx=arctan(Ii/Ri)×(180°/π)+360°；
Where x is "test" or system and the corresponding i is "calibration" or "test".
Preferably, the calculating of the rate of change of the amplitude relative error with the resistance value REA _ S and the rate of change of the curve shape relative error with the resistance value RES _ S of each two sets of adjacent measurement data includes:
wherein the content of the first and second substances,
(ii) a m is the number of the measuring frequency points;
is the feedback resistance R of the jth positionFBUsing the impedance amplitude Z and the impedance phase respectively for the ith frequency point data in the calibration data measured below
One or more items of the impedance real part G and the impedance imaginary part X are used as a specific parameter expression to replace a letter S, wherein X is the impedance imaginary part of the finally corrected measurement data, and Z is the impedance amplitude in the finally corrected measurement data;
is the feedback resistance R of the jth positionFBAveraging the measured corrected measurement data; Δ R is a difference between the resistance values of the feedback resistor at the j +1 th position and the feedback resistor at the j th position;
the standard deviation of the corrected measured data measured under the feedback resistance of the jth position; REA _ SjThe corrected amplitude value of the measurement data of the feedback resistor at the jth position is a relative error change rate value; CCD _ SjThe cross-correlation distance between the measurement data of the feedback resistance at the jth position and the measurement data of the feedback resistance at the (j + 1) th position; RES _ SjThe shape of the measured data curve for the feedback resistance at the jth location is the relative error rate value.
Preferably, the score calculation formula of the feedback resistance is as follows:
Pjscore of feedback resistance for jth position;
the first score of the feedback resistance of the jth position under the REA _ S index;
a second score for the feedback resistance at the jth location under the RES _ S index; alpha is alpha1A scoring weight for the measured data amplitude versus the rate of change of error; alpha is alpha2A scoring weight for the shape of the measurement data curve versus the rate of change of error; alpha is alpha1And alpha2Setting according to the selection requirement bias of the measurement data amplitude and the curve shape; s = Z,
G or X.
In a second aspect, the present invention further provides an improved miniature impedance measurement self-calibration apparatus for implementing the improved miniature impedance measurement self-calibration method of the first aspect, the apparatus comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor for performing the improved miniature impedance measurement self-calibration method of the first aspect.
In a third aspect, the present invention also provides a non-transitory computer storage medium having stored thereon computer-executable instructions for execution by one or more processors for performing the improved miniature impedance measurement self-calibration method of the first aspect.
The invention comprehensively considers the relative change of the impedance amplitude and the impedance phase when selecting the measurement data, and the selected data is the measurement data with the best calibration effect under the condition of comprehensively considering the precision of the impedance amplitude and the precision of the impedance phase.
Compared with the data measured by the gain coefficient calibration method, the method has the advantage that the accuracy of the selected measurement data on the basis of the traversal calculation and analysis of all the measured data is improved in the impedance amplitude and the impedance phase.
Furthermore, the measurement data with the best calibration effect selected by the invention is a complete set of measurement data, the phenomenon of record overflow on individual frequency points can not occur, and the obtained measurement data can be ensured to be continuous data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow chart of an improved miniature impedance measurement self-calibration algorithm provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of an example architecture of an improved miniature impedance measurement self-calibration algorithm provided by an embodiment of the present invention;
FIG. 3 is a schematic flow chart of an improved miniature impedance measurement self-calibration algorithm provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of a system architecture for improved miniature impedance measurement self-calibration according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a partial structure of an improved miniature impedance measurement self-calibration system architecture provided by an embodiment of the present invention;
fig. 6 is an example of a gain coefficient table of a gain coefficient calibration method according to an embodiment of the present invention;
FIG. 7 is a flowchart illustrating a conventional gain factor calibration method according to an embodiment of the present invention;
FIG. 8 is a graph illustrating the measurement of the impedance magnitude of a gain factor calibration method according to an embodiment of the present invention;
FIG. 9 is a schematic flow chart of an improved miniature impedance measurement self-calibration algorithm provided by an embodiment of the present invention;
fig. 10 is a table of quadrant correction relationship of impedance phase according to an embodiment of the present invention;
FIG. 11 is a table of the calculation index ranking scores of the self-calibration algorithm according to the embodiment of the present invention;
FIG. 12 is an impedance magnitude measurement of a self-calibration algorithm provided by an embodiment of the present invention;
FIG. 13 is an impedance phase measurement of a self-calibration algorithm provided by an embodiment of the present invention;
FIG. 14 is a graph comparing impedance magnitude measurements for a self-calibration algorithm provided by an embodiment of the present invention with a conventional gain factor calibration method;
FIG. 15 is a graph comparing the measured impedance magnitude data and the measured impedance magnitude data with the accurate data according to the two methods;
FIG. 16 is a schematic structural diagram of an improved miniature impedance measurement self-calibration apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
in step 201, the measured data is corrected according to an impedance correction formula, and all the corrected data are arranged from small to large according to the magnitude of the feedback resistance value used.
In a certain implementation process, the object to be measured can be calibrated and measured based on the AD5933 with different feedback resistances, and calibration data in a calibration stage and measurement data in a measurement stage under multiple sets of feedback resistances are obtained.
In step 202, the rate of change of the amplitude relative error with the resistance value REA _ S and the rate of change of the curve shape relative error with the resistance value RES _ S of each two groups of corrected adjacent measurement data are calculated respectively. For example, as shown in FIG. 2, the corrected neighbor measurement data is shown as four sets of data for one resistor in FIG. 2, where the values of the character S include Z,
G and X, Z is the impedance modulus M using the gain factor GFMeasuringCorrected impedance amplitude theta of the object to be measured and the phase theta of the systemsystemImpedance phase theta to object to be measuredMeasuringAfter the correction, G is the real impedance part of the final corrected measurement data, and X is the imaginary impedance part of the final corrected measurement data, wherein the real and imaginary impedance parts are formed by Z and
obtained by conversion according to the formula: g = Z × cos θ, X = Z × sin θ. RES _ S and REA _ S here represent the relative deviation of one entire set of data from another, and RES _ S is represented as RES _ Z, RES \/in FIG. 2
RES _ G and RES _ X, RES _ X in FIG. 2i-1Represented by the resistance Ri-1Is as follows
And R isiIs as follows
Relative deviation, other parameter expressions are analogized in turn.
In step 203, selecting four resistors with the smallest amplitude relative error change rate REA _ S to form a candidate resistor interval RI1, and calculating a first score of each resistor according to the relative magnitude of the amplitude relative error change rate REA _ S; four resistors with the smallest curve shape relative error change rate RES _ S are selected to form a candidate resistor section RI2, and a second score of each resistor is calculated according to the relative size of the curve shape relative error change rate RES _ S.
In the example shown in FIG. 2, the selection is made around Z,
G and X dimensions are used for sequencing the change rate of the amplitude relative error along with the resistance value and sequencing the change rate of the curve shape relative error along with the resistance value; in an optional implementation manner, the ranking of one or more items may be selected for weighted calculation to obtain a final ranking result. In embodiment 2 of the present invention, an example of a solution that directly adopts a Z single dimension as a sequence for calculating a change rate of an amplitude relative error with a resistance value will also be presented, and details are not repeated here.
In step 204, the selected candidate resistance section RI1 and the candidate resistance section RI2 are merged to form the candidate resistance section RI, and the first score of each resistance in the candidate resistance section RI at the amplitude change rate and the second score at the curve shape change rate are summed.
A specific example of a corresponding calculation method may refer to the architectural diagram presented in fig. 2; and corresponding implementation details will also be specifically set forth in the following extended embodiments of the present invention.
In step 205, the resistor with the highest score after summation is selected as the best feedback resistor.
The resistance is the calibration resistance of the miniature impedance measuring device which is most suitable for the measuring object. The calibration resistor can be used to help the miniature impedance measuring device to perform calibration and measurement.
The embodiment of the invention comprehensively considers the relative change of the impedance amplitude and the impedance phase when selecting the measurement data, and the selected data is the measurement data with the best calibration effect under the condition of comprehensively considering the precision of the impedance amplitude and the precision of the impedance phase.
Furthermore, the measurement data with the best calibration effect selected by the invention is a complete set of measurement data, the phenomenon of record overflow on individual frequency points can not occur, and the obtained measurement data can be ensured to be continuous data.
In the embodiment of the present invention, in step 205, a complex situation may also occur, that is, it is determined whether the selected optimal feedback resistance is unique, and if the number of the resistors with the highest score after the summation is not unique, the method further includes:
and selecting the resistor with the corresponding resistance value closest to the resistance value at the center position of the candidate resistor section RI from the plurality of optimal feedback resistors.
With reference to the embodiment of the present invention, the measured data related to step 201 in embodiment 1 is corrected according to an impedance correction formula, and the development includes a calibration stage and a measurement stage, as shown in fig. 3, specifically:
taking the test circuit architecture shown in fig. 4 as an example, the impedance real part and the impedance imaginary part at each frequency point can be obtained after inputting the voltage Vin to the circuit and collecting the output signal Vout, and then performing fourier decomposition on the Vout signal, that is, the impedance real part data R can be obtained by the micro impedance measurement device during the calibration phaseSchoolImpedance imaginary part data ISchool(ii) a Then, the measurement stage will measure the real part of impedance data RMeasuringAnd impedance imaginary data IMeasuring。
In a specific implementation process, in order to improve the switching efficiency of the feedback resistors, a feedback resistor assembly shown in fig. 5 may be adopted, a plurality of selectable feedback resistors are built in advance by using selection switches, and the requirements that different feedback resistors are connected to the test circuit are met by closing the corresponding selection switches.
In step 2011, during the calibration phase, the feedback resistor R is connectedFBConnecting calibration resistors R with equal resistance valuescalAcquiring a first real part value R of the obtained calibration resistorSchoolAnd a first imaginary value ISchoolObtaining the modulus M of the calibration resistor according to the calculationSchoolCalculating the gain factor GF and the system phase thetasystem。
In step 2012, in the measurement phase, the calibration resistor is replaced by the object to be measured, and then the returned second real part value R is collectedMeasuringAnd a second imaginary value IMeasuringCalculating to obtain the module value M of the object to be measuredMeasuringUsing gain factor GF versus impedance modulus MMeasuringMaking corrections and using the system phase θsystemAnd correcting the impedance phase of the object to be measured.
Wherein a gain factor GF and a system phase theta are calculatedsystemThe calculation formula is as follows:
GF=(1/Rcal)/Mschool；（1）
θsystem=arctan(ISchool/RSchool)×180°/π；（2）
RcalTo calibrate the resistance value of the resistor, a known value;
the impedance modulus value of the calibration resistor measured in the calibration stage; i isSchoolFor the imaginary part of the impedance, R, of the calibration resistance measured during the calibration phaseSchoolThe real part of the impedance of the calibration resistor measured in the calibration stage.
Correspondingly, the gain factor GF is used to match the impedance modulus MMeasuringMaking corrections and using the system phase θsystemCorrecting the impedance phase of the object to be detected, wherein the specific correction formula is as follows:
Z=(1/GF)/Mmeasuring=(MSchool×Rcal)MMeasuring；（3）
θMeasuring=arctan(ISide survey/RMeasuring)×(180°/π)；（4）
Wherein the content of the first and second substances,
the impedance module value of the object to be measured is measured in the measuring stage; z is the impedance modulus M using the gain factor GFMeasuringCorrecting the impedance amplitude of the object to be detected; I.C. AMeasuringThe impedance imaginary part of the object to be measured is measured in the measuring stage; rMeasuringThe real impedance part of the object to be measured is measured in the measuring stage; theta.theta.MeasuringThe impedance phase is calculated according to the real impedance part of the object to be measured and the imaginary impedance part of the object to be measured which are measured in the measuring stage.
Wherein, the range of the arctan function is [ -pi/2, pi/2]And the range of the actual phase is [ -pi, pi]And thus the measured phase thetasystemAnd thetaMeasuringQuadrant correction of the phase is required;
end use
The phase data of (2) also needs to be corrected by a system phase, and the formula of the system phase correction is as follows: θ = θMeasuring-θsystem(ii) a (5) Where θ is the precise impedance phase. According to the formula, the miniature impedance measuring equipment can be calibrated and used for measurement, and the measurement data of the miniature impedance measuring equipment is corrected into accurate data, so that the miniature impedance measuring equipment can be used for high-precision measurement.
The quadrant correction comprises:
first quadrant, θx=arctan(Ii/Ri)×(180°/π)；
Second quadrant, θx=arctan(Ii/Ri)×(180°/π)+180°；
Third quadrant, θx=arctan(Ii/Ri)×(180°/π)+180°；
Fourth quadrant, θx=arctan(Ii/Ri)×(180°/π)+360°；
Where x is "test" or system and the corresponding i is "calibration" or "test".
With reference to the embodiment of the present invention, in step 202, a rate of change of amplitude relative error with respect to resistance value REA _ S and a rate of change of curve shape relative error with respect to resistance value RES _ S of each two sets of adjacent measurement data are calculated, respectively, and a specific implementation manner is also provided in the embodiment of the present invention, including:
wherein the content of the first and second substances,
(ii) a m is the number of the measuring frequency points;
is the feedback resistance R of the jth positionFBThe ith frequency point data in the calibration data measured below may use impedance magnitude, impedance phase, impedance real part and impedance imaginary part data, respectively, instead of the expression S, i.e., S = Z,
G or X;
wherein the content of the first and second substances,
is the feedback resistance R of the jth positionFBAveraging the measured corrected measurement data; Δ R is a difference between the resistance values of the feedback resistor at the j +1 th position and the feedback resistor at the j th position;
the standard deviation of the corrected measured data measured under the feedback resistance of the jth position; REA _ SjThe corrected amplitude value of the measurement data of the feedback resistor at the jth position is a relative error change rate value; CCD _ SjThe cross-correlation distance between the measurement data of the feedback resistance at the jth position and the measurement data of the feedback resistance at the (j + 1) th position; RES _ SjThe measured data curve of the feedback resistance for the jth positionRelative error rate of change of shape, S = Z,
G or X. Wherein, I above is the imaginary impedance part returned by the measurement, and I is not the final correct imaginary impedance part; where X is the correct imaginary impedance after final correction; m is the impedance modulus, Z is the impedance amplitude obtained after final correction, and M and Z can be obtained by GF conversion.
In combination with the embodiment of the present invention, there is also provided a score calculation method for a feedback resistor, where the corresponding calculation formula is as follows:
Pjscore for feedback resistance at jth position;
the first score of the feedback resistance of the jth position under the REA _ S index;
a second score for the feedback resistance at the jth location under the RES _ S index; alpha is alpha1A scoring weight for the measured data amplitude versus error rate; alpha is alpha2A scoring weight for the shape of the measurement data curve versus the rate of change of error; alpha is alpha1And alpha2Setting according to the selection requirement weight of the measured data amplitude and the curve shape; s = Z,
G or X, in embodiments of the invention, optionally α1=α2=0.5。
Example 2
In the field of structural health monitoring, when a micro impedance measuring device is used to perform damage monitoring based on an impedance method, a Piezoelectric ceramic Transducer (PZT) is generally connected to the micro impedance measuring device, the PZT is then attached to an object to be monitored, and the micro impedance measuring device can automatically perform impedance measurement on the object to be monitored based on a specific algorithm. The traditional algorithm for helping the miniature impedance measuring equipment to carry out automatic measurement is a gain coefficient calibration method, and the difference between the gain coefficient calibration method and the self-calibration algorithm of the invention is respectively compared in detail below.
Firstly, 25 feedback resistors are used, impedance in a frequency range of [50kHz, 60kHz ] is measured on an iron bar by adopting a gain coefficient calibration method, the gain coefficient method is an algorithm suitable for being embedded into a single chip microcomputer for use, then because the memory space and the calculation capacity of the single chip microcomputer are limited, the gain coefficient only can be stored under a certain specific frequency point, and the frequency point full coverage of the gain coefficient cannot be realized, so that the problem that the frequency value of the used gain coefficient is not in the range of the measured frequency interval cannot be avoided in the actual impedance measurement, therefore, in order to be consistent with the actual application situation, the gain coefficient under the frequency point of 10kHz is selected for use in the embodiment of the invention, the used feedback resistors, the impedance measurement range and the gain coefficient are as shown in figure 6, and the measurement module value range under the feedback resistors can be calculated according to the range of the impedance and the gain coefficient, the modulus value ranges are calculated as follows (for convenience of self-alignment of formula numbers in example 1 and example 2, and for easy viewing, formula numbers start from 1 in example 1, and from 21 in example 2):
Mx=(1/GF)/Zx=1/(GF×Zx) (ii) a (21) Wherein GF is the gain factor, ZxIs the impedance amplitude, M, of the object to be measuredxIs the impedance modulus of the object to be measured;
wherein, ZxIn relation to the parameter setting of the measuring device, taking the system architecture schematic diagram of the improved micro impedance measurement self-calibration as an example of the measuring device shown in FIG. 4, the output signal is
Thus, ZxAnd measureThe relationship of the parameter settings of the measuring device is as follows:
substituting the formula (23) into the formulas (21) and (22) results in the following relationship:
the output voltage of the measuring device needs to satisfy a certain range, which can be obtained from the formula (24), when the setting of the measuring parameter is fixed (e.g. Programmable Gain Amplifier, abbreviated as PGA; and the input voltage VinFixed), may be based on the gain factor GF, the feedback resistance value RFBCalculating to obtain the module value measuring ranges corresponding to different feedback resistors, and MmaxAnd Mmin。
The measurement flow of the gain factor calibration method is shown in fig. 7.
Firstly, the feedback resistor switched to the minimum gear (for example, the feedback resistor 470 Ω used here) performs impedance measurement on the object to be measured at a first frequency point (for example, 50 kHz), returns to the measured module value, then judges whether the measured module value is within the module value range of the feedback resistor 470 Ω, if not, switches to the feedback resistor of the next gear to perform re-measurement and judgment until the measured module value is within the module value range of the feedback resistor used, and then calculates the impedance value according to the gain coefficient under the feedback resistor of the gear:
Z=(1/GF)/Mx=1/(GF×Mx) (ii) a Wherein M is a measured modulus value;
and if the module values measured by the frequency point under the feedback resistors of all the gears are not in the corresponding module value range, marking the frequency point to record overflow. And finally, recording and outputting the calculated impedance value, and repeating the process to measure the impedance of the next frequency point.
The measurement result of the above-mentioned prior art gain factor calibration method is shown in fig. 8, and it can be seen that there is a certain error between the measured data and the accurate data, and it can be seen that, as labeled in fig. 8, there are recording overflows between points 1 and 2 (represented in the figure as two adjacent points on the measured data line, and an interruption of the measured data occurs between them), between points 3 and 4, between points 5 and 6, and between points 7 and 8, which results in a discontinuity of the measured impedance magnitude curve. Moreover, the algorithm only returns impedance magnitude data, and does not consider and return impedance phase data. Therefore, a high-precision measurement method is needed in which the measurement data are continuous and the impedance amplitude and impedance phase data can be returned simultaneously.
Based on the improved micro impedance measurement self-calibration algorithm provided by the embodiment 1, the embodiment of the invention further combines the existing gain coefficient calibration algorithm scene introduced above to explain the innovative implementation process in the corresponding scene of the technical scheme of the invention, thereby presenting the improved effect of the invention in a comparative way. The invention adopts the algorithm to carry out [50kHz, 60kHz ] on the iron bar by using 25 feedback resistors]The impedance over the frequency range is measured. Firstly, one of the feedback resistors is selected to calibrate the miniature impedance measuring equipment, and the real impedance part R of the feedback resistor in the calibration stage is acquiredSchoolAnd an imaginary impedance ISchoolData, then the change-over switch is connected with the object to be measured for measurement, and the real part R of the impedance of the object to be measured in the measurement stage is collectedMeasuringAnd an imaginary impedance IMeasuringAnd (4) data. And sequentially switching different calibration resistors to perform calibration and measurement, and repeating the processes. Real part of impedance R of feedback resistor in all calibration stagesSchoolAnd an imaginary impedance ISchoolData and corresponding real part of impedance R of object to be measuredMeasuringAnd an imaginary impedance IMeasuringAfter the data is collected, all the measured data are processed and analyzed by using the self-calibration algorithm of the invention, and the calculation flow chart of the algorithm is shown in fig. 9 and comprises the following steps:
in step 301, measurement data is collected and corrected according to an impedance correction formula.
Calibration phase in the correction process: as shown in FIG. 4, ZxPosition connecting a calibration resistor with the resistance value equal to that of the feedback resistor, and then acquiring the real impedance value R of the calibration resistorSchoolAnd the imaginary value of the impedance ISchoolThen according to the calculated impedance modulus value M of the calibration resistorSchoolCalculating the gain factor GF and the system phase thetasystemThe calculation formula is as formula (1) to formula (2) in example 1.
Measurement phase in the correction process: as shown in FIG. 4, ZxConnecting the position of the object to be measured, and then collecting the real part value R of the impedance of the returned object to be measuredMeasuringAnd the imaginary value of the impedance IMeasuringThen calculating to obtain the impedance modulus M of the object to be measuredSide surveyFinally, the gain coefficient obtained in the calibration stage is used for correcting the impedance module value of the object to be measured, and the correction formula is shown as the formula (3) in the embodiment 1; using RMeasuringAnd IMeasuringCalculating the impedance phase of the object to be measured in the measurement stage, and using the system phase thetasystemFor impedance phase thetaMeasuringThe correction was performed as in formula (4) to formula (5) in example 1.
Since the range of the arctan function is [ - π/2, π/2]And the range of the actual phase is [ -pi, pi]And therefore the above measured phase thetasystemAnd thetaMeasuringQuadrant correction is required, and the quadrant correction relationship of the phase is shown in fig. 10 and described in detail in embodiment 1.
According to the formula, the micro impedance measuring equipment can be calibrated and used for measurement, and the measurement data of the micro impedance measuring equipment is corrected into accurate data, so that high-precision measurement can be realized by using the micro impedance measuring equipment.
In step 302, all the corrected data are arranged from small to large according to the magnitude of the feedback resistance value used.
In step 303, the rate of change of the amplitude relative error with the resistance value REA _ S and the rate of change of the curve shape relative error with the resistance value RES _ S of each two sets of adjacent data are calculated, respectively.
And (3) calculating formulas of the change rate of the amplitude relative error of the adjacent data and the change rate of the curve shape relative error along with the resistance value, wherein the calculation formulas are shown as formulas (6) - (7).
In step 304, selecting four resistors with the minimum rate of change of the amplitude relative error along with the resistance value to form a candidate interval RI1, and calculating the score of each resistor according to the relative magnitude of the rate of change; the four resistors having the smallest rate of change in the relative error of the curve shape with respect to the resistance values are selected to constitute the candidate section RI2, and the score for each resistor is calculated from the relative magnitude of the rate of change, and the score for each resistor is shown in fig. 11.
In step 305, the candidate resistance sections RI are formed by taking a union of the candidate resistance sections RI1 and RI2 selected as described above, and the score of each resistance in the candidate resistance section RI at the amplitude change rate and the score at the curve shape change rate are summed up.
In step 306, the summed resistance with the highest score is selected as the best feedback resistance.
In step 307, it is determined whether the selected optimal feedback resistance is unique, and if the number of the selected optimal feedback resistances is not unique, a resistance having a resistance value closest to the resistance value of the center position of the candidate resistance interval among the optimal feedback resistances is continuously selected.
In this experiment, the measurement result of the impedance amplitude using the self-calibration algorithm of the present invention is shown in fig. 12, and the measurement result of the impedance phase is shown in fig. 13. A comparison graph of the impedance amplitude measurement results of the gain coefficient calibration method and the self-calibration algorithm of the present invention is shown in fig. 14, which shows that the curve obtained by measuring the gain coefficient calibration method is discontinuous due to the recording overflow phenomenon during the measurement process, for example, the curve obtained by measuring the gain coefficient calibration method is discontinuous between 1 and 2 points, between 3 and 4 points, between 5 and 6 points, and between 7 and 8 points. In addition, the comparison result of the relative error between the impedance amplitude data and the accurate data measured by the two methods is shown in fig. 15, so that the accuracy of the impedance amplitude is further improved by the self-calibration algorithm provided by the invention, and the method provided by the invention can return impedance phase data, thereby being beneficial to performing damage monitoring based on the impedance amplitude and the impedance phase in the field of structural health monitoring.
The embodiment of the invention comprehensively considers the relative change of the impedance amplitude and the impedance phase when selecting the measurement data, and the selected data is the measurement data with the best calibration effect under the condition of comprehensively considering the precision of the impedance amplitude and the precision of the impedance phase.
Compared with the data measured by the gain coefficient calibration method, the method has the advantage that the accuracy of the selected measurement data on the basis of the traversal calculation and analysis of all the measured data is improved in the impedance amplitude and the impedance phase.
Furthermore, the measurement data with the best calibration effect selected by the invention is a group of complete measurement data, the phenomenon of record overflow on individual frequency points can not occur, and the obtained measurement data can be ensured to be continuous data.
Example 3
Fig. 16 is a schematic diagram of an improved miniature impedance measurement self-calibration apparatus according to an embodiment of the present invention. The improved miniature impedance measurement self-calibration apparatus of the present embodiment includes one or more processors 21 and a memory 22. In fig. 16, one processor 21 is taken as an example.
The processor 21 and the memory 22 may be connected by a bus or other means, and the bus connection is exemplified in fig. 16.
The memory 22, which is a non-volatile computer readable storage medium, may be used to store non-volatile software programs and non-volatile computer executable programs, such as the improved miniature impedance measurement self-calibration method of embodiment 1. The processor 21 performs the improved miniature impedance measurement self-calibration method by executing non-volatile software programs and instructions stored in the memory 22.
The memory 22 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 22 may optionally include memory located remotely from the processor 21, and these remote memories may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The program instructions/modules stored in the memory 22, when executed by the one or more processors 21, perform the improved miniature impedance measurement self-calibration method of embodiment 1 above, e.g., perform the various steps shown in fig. 1-3 described above.
It should be noted that, because the contents of information interaction, execution process, and the like between modules and units in the apparatus and the system are based on the same concept as the processing method embodiment of the present invention, specific contents may refer to the description in the method embodiment of the present invention, and are not described herein again.
Those of ordinary skill in the art will appreciate that all or part of the steps of the various methods of the embodiments may be implemented by associated hardware as instructed by a program, which may be stored on a computer-readable storage medium, which may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. An improved miniature impedance measurement self-calibration algorithm, comprising:
correcting the measured data of the resistor to be measured according to an impedance correction formula, and arranging all the corrected data from small to large according to the size of the used feedback resistance value;
respectively calculating the change rate of the amplitude relative error of every two adjacent groups of corrected measurement data along with the change rate of the feedback resistance value and the change rate of the curve shape relative error along with the change rate of the feedback resistance value;
selecting four resistors with the minimum amplitude relative error change rate along with the change rate of the feedback resistance value to form a candidate resistor interval RI1, and calculating a first score of each resistor according to the relative magnitude of the amplitude relative error change rate along with the change rate of the resistance value; selecting four resistors with the smallest change rate of the curve shape relative error along with the resistance value to form a candidate resistor interval RI2, and calculating a second score of each resistor according to the relative size of the curve shape relative error along with the change rate of the resistance value;
combining the selected candidate resistor section RI1 and the candidate resistor section RI2 to form a candidate resistor section RI, and summing a first score of each resistor in the candidate resistor section RI under the amplitude relative error change rate and a second score under the curve shape relative error change rate;
and selecting the resistor with the highest score after summation as the optimal feedback resistor.
2. The improved impedance measurement self-calibration algorithm of claim 1, wherein the method further comprises determining whether the selected optimal feedback resistance is unique, and if the number of the summed resistances with the highest score is not unique:
and selecting the resistor with the corresponding resistance value closest to the resistance value at the center position of the candidate resistor section RI from the plurality of optimal feedback resistors.
3. The improved miniature impedance measurement self-calibration algorithm according to claim 1, wherein the measurement data of the resistor to be measured is corrected according to an impedance correction formula, and the algorithm comprises a calibration stage and a measurement stage, specifically:
in the calibration phase, the feedback resistor R is connectedFBConnecting calibration resistors R with equal resistance valuescalAcquiring a first real part value R of the obtained calibration resistorSchoolAnd a first imaginary value ISchoolObtaining the modulus M of the calibration resistor according to the calculationSchoolCalculating the gain factor GF and the system phase thetasystem；
In the measuring stageReplacing the calibration resistor with an object to be measured, and then collecting a returned second real part value RMeasuringAnd a second imaginary value IMeasuringAnd calculating to obtain the impedance modulus M of the object to be measuredSide surveyUsing gain factor GF versus impedance modulus MMeasuringMaking corrections and using the system phase θsystemAnd correcting the impedance phase of the object to be measured.
4. The improved miniature impedance measurement self-calibration algorithm of claim 3 wherein the calculation of the gain factor GF and the system phase θsystemThe calculation formula is as follows:
GF=(1/Rcal)/Mschool；
θsystem=arctan(ISchool/RSchool)×180°/π；
RcalTo calibrate the resistance value of the resistor, a known value;
the impedance modulus of the calibration resistor measured in the calibration stage; i isSchoolFor the imaginary part of the impedance, R, of the calibration resistance measured during the calibration phaseSchoolThe real part of the impedance of the calibration resistor measured in the calibration stage.
5. The improved miniature impedance measurement self-calibration algorithm of claim 4, wherein gain factor GF is used to model impedance value MMeasuringAnd correcting to obtain a corrected impedance amplitude Z, wherein the specific correction formula is as follows:
Z=(1/GF)/Mmeasuring=(MSchool×Rcal)MMeasuring；
Wherein the content of the first and second substances,
the impedance module value of the object to be measured is measured in the measuring stage; z is the impedance modulus M using the gain factor GFMeasuringCorrecting the impedance amplitude of the object to be detected; i isMeasuringAn impedance imaginary part of the object to be measured in the measurement stage; rMeasuringThe real impedance part of the object to be measured in the measuring stage.
6. The improved impedance measurement self-calibration algorithm according to claim 5, wherein the impedance phase θ is calculated according to the real impedance part of the object to be measured and the imaginary impedance part of the object to be measured in the measurement phaseMeasuringComprises the following steps:
θmeasuring=arctan(IMeasuring/RMeasuring)×(180°/π)；
And using the system phase thetasystemImpedance phase theta of object to be measuredSide surveyAnd correcting by using a system correction formula as follows: θ = θSide survey-θsystem(ii) a Where θ is the precise impedance phase.
7. The improved miniature impedance measurement self-calibration algorithm of claim 6 wherein the range of values due to the arctan function is [ -pi/2, pi/2]And the range of the actual phase is [ -pi, pi]Thus the system phase θsystemAnd impedance phase thetaMeasuringQuadrant correction of the phase is required; the quadrant correction comprises:
first quadrant, θx=arctan(Ii/Ri)×(180°/π)；
Second quadrant, θx=arctan(Ii/Ri)×(180°/π)+180°；
Third quadrant, θx=arctan(Ii/Ri)×(180°/π)+180°；
Fourth quadrant, θx=arctan(Ii/Ri)×(180°/π)+360°；
Where x is "test" or system and the corresponding i is "calibration" or "test".
8. The improved miniature impedance measurement self-calibration algorithm of claim 1, wherein the calculating of the rate of change of the relative error of the amplitude value with the resistance value REA _ S and the rate of change of the relative error of the curve shape with the resistance value RES _ S for each two sets of adjacent measurement data respectively comprises:
wherein the content of the first and second substances,
(ii) a m is the number of the measuring frequency points;
is the feedback resistance R of the jth positionFBUsing the impedance amplitude Z and the impedance phase respectively for the ith frequency point data in the calibration data measured below
One or more items of the impedance real part G and the impedance imaginary part X are used as a specific parameter expression to replace a letter S, wherein X is the impedance imaginary part of the finally corrected measurement data, and Z is the impedance amplitude in the finally corrected measurement data;
is the feedback resistance R of the jth positionFBAveraging the measured corrected measurement data; Δ R is a difference between the resistance values of the feedback resistor at the j +1 th position and the feedback resistor at the j th position;
the standard deviation of the corrected measured data measured under the feedback resistance of the jth position; REA _ SjThe corrected amplitude value of the measurement data of the feedback resistor at the jth position is a relative error change rate value; CCD _ SjThe cross-correlation distance between the measurement data of the feedback resistance at the jth position and the measurement data of the feedback resistance at the (j + 1) th position; RES _ SjThe shape of the measured data curve for the feedback resistance at the jth location is the relative error rate value.
9. The improved miniature impedance measurement self-calibration algorithm of claim 1 wherein the score of the feedback resistance is calculated by the formula:
Pjscore of feedback resistance for jth position;
the first score of the feedback resistance of the jth position under the REA _ S index;
a second score for the feedback resistance at the jth location under the RES _ S index; alpha is alpha1A scoring weight for the measured data amplitude versus error rate; alpha (alpha) ("alpha")2A scoring weight for the shape of the measurement data curve versus the rate of change of error; alpha is alpha1And alpha2Setting according to the selection requirement bias of the measurement data amplitude and the curve shape; s = Z,
G or X; wherein Z is an impedance amplitude,
Is the impedance phase, G is the real part of the impedance and X is the imaginary part of the impedance; REA _ S is the rate of change of the relative error of the amplitude of each two adjacent sets of measured data with the resistance value, and RES _ S is the rate of change of the relative error of the curve shape of each two adjacent sets of measured data with the resistance value.
10. An improved miniature impedance measurement self-calibration apparatus, said apparatus comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor for performing the improved miniature impedance measurement self-calibration algorithm of any of claims 1-9.
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