CN110530253A - Optimum design method for resistance-type wireless and passive strain transducer measuring circuit - Google Patents

Abstract

The invention relates to an optimal design method for a resistance-type wireless passive strain sensor measuring circuit, comprising: (1) analyzing the characteristics of the resistance-type wireless passive strain sensor measuring circuit according to Kirchhoff's law, and establishing the relationship between the resistance value of the strain sensor and the circuit The relational expression of the total input impedance; (2) use the multi-objective optimization model to optimize each parameter to obtain more significant system input impedance phase information; (3) use the multi-objective optimization function gamultiobj in the Matlab software to solve the step ( 2) The optimization model in , solves the design variable A; (4) According to the design variable A obtained in step (3) and the Pareto front-end diagram generated by Matlab software, the optimized system input impedance phase can be obtained at different strain resistance values The relationship between the change of the system input impedance phase and the frequency before the optimization is compared with the relationship between the system input impedance phase and the frequency change before optimization, and the improvement effect of the optimized parameters on the relationship between the system input impedance phase and the frequency change is explained.

Description

Optimal design method for measuring circuit of resistive wireless passive strain sensor

technical field

The invention belongs to the field of wireless passive strain sensors, and in particular relates to an optimal design method for a measuring circuit of a resistive wireless passive strain sensor, which can be used to realize the optimal design of each parameter in a resistive wireless passive strain measuring circuit system.

Background technique

Structural health monitoring technology is often used to assess the structural health of the system and its own damage and aging. For example, it is widely used in the fields of infrastructure health assessment, industrial manufacturing, and biomedicine, and can prevent possible structural damage accidents in advance. Measures to reduce personal and property losses as much as possible.

Among all the structural health monitoring quantities, strain is the most commonly used monitoring quantity, because it can directly reflect the load information borne by the structure. The common strain monitoring process is to use strain sensors to obtain information about the strain characteristics of the system under test, and use this information to evaluate the health of the structure, so as to avoid possible structural damage accidents. However, some measurement environments are harsh, limiting the use of batteries or wired connections. For example, in environments without external access points, or in the case of implanted measurement devices in closed environments, the use of batteries and wired-connected sensor components must be avoided. Therefore, research on wireless measurement methods for strain is valuable.

The existing wireless strain measurement methods mainly include four kinds: surface acoustic wave sensor, inductive coupling characteristic, patch antenna and digital image technology.

However, the cost of surface acoustic wave sensor and patch antenna strain measurement scheme is relatively high, and the requirements for manufacturing process and precision are relatively high. Solutions based on digital image technology also require professional high-frequency scanning and camera equipment, especially when the measurement process requires the layout of multiple monitoring equipment at different locations, the cost will be extremely high. The wireless strain measurement solution based on inductive coupling characteristics can not only realize non-contact strain monitoring, but also theoretically has the characteristics of infinite life, simple structure, and reliable performance. It is very suitable for measurement environments such as closed spaces and mechanical power rotating parts. However, due to the exponential decay of the inductive coupling magnetic field with the increase of distance, the wireless passive strain sensor designed by using the inductive coupling characteristic will face the problem of limited monitoring distance, and the actual measurement effect of strain characteristic information is not significant.

Existing methods to improve the limited monitoring distance mainly include the following: utilizing left-handed materials or relay coil schemes, optimizing the quality factor of sensor coils, and using piezoelectric polymer materials to amplify strain characteristic information.

(1) Use relay coils to improve sensor readout performance. For example, in the paper Diego A.Sanz, CostantinoMitrosbaras, Edgar A.Unigarro, Fredy Segura-Quijano. Passive resonators for wireless passive sensor readout enhancement. Applied Physics Letters, 2013, 103(13): 133502. Proposed the use of relay coils to improve wireless passive sensor readout enhancement. The readout performance of the source sensor.

(2) Using left-handed materials to improve sensor readout performance. For example, in the paper Bingnan Wang, Koon Hoo Teo, Tamotsu Nishino, William Yerazunis. Experiments on wireless power transfer with metamaterials. Applied Physics Letters, 2011, 98(25): 254101. A left-handed double-layer coil structure with negative magnetic permeability is proposed. material, which itself has larger inductance and capacitance values, thereby improving the readout performance of the sensor.

(3) Optimize the quality factor of the sensor coil. Such as the paper Hao Jiang, Di Lan, Hamid Shahnasser, Shuvo Roy. Sensitivity analysis of an implantable LC based passive sensor. International Conference on Biomedical Engineering and Informatics. 2010, 4:1586-1590. Established an implantable sensor based on LC resonator The sensitivity analysis model is optimized to obtain the maximum inductor quality factor and sensor sensitivity, so as to achieve the purpose of enhancing the readout performance of the sensor.

(4) Use piezoelectric polymer materials to amplify the readout effect of the sensor. For example, in the paper Sun Ke.Design and characterization of a passive wireless strain sensor.University of Puerto Ricoat Mayagüez.2006. It is proposed to attach a layer of piezoelectric polymer as a dielectric layer on the top of the interdigital capacitor to amplify the strain characteristics, thereby improving the sensor Sensitivity, enabling enhanced sensor readout performance.

However, in the above studies, the improvement schemes are all based on the sensor part, but there is no optimal design method for the entire measurement system including the readout coil, load resistance and resonance frequency. Since the measurement of wireless passive strain depends on the inductive coupling between two inductors to form a wireless passive strain measurement system to realize the transmission of strain characteristic information, this paper proposes a resistive wireless Optimal design method of source strain sensor measurement circuit.

Contents of the invention

Purpose of the invention: The present invention discloses an optimal design method for the measurement circuit of a resistive wireless passive strain sensor, in order to solve the problem that the wireless passive strain sensor designed using inductive coupling characteristics has a limited monitoring distance and the actual measurement effect of strain characteristic information is not significant question. The core of this design method is, firstly, based on the circuit principle, establish the relationship between the resistance value of the strain sensor and the total input impedance of the circuit; Taking the maximum value of the phase change ΔPhase _Zin and the sensory inductance quality factor Q as the objective function, an optimal design model for the parameters of the resistive wireless passive strain sensor measurement circuit is established; moreover, the constraint conditions are set reasonably and the multi-objective optimization solution method is used to obtain The values of each parameter after optimization are obtained.

Technical solution: an optimal design method for resistive wireless passive strain sensor measurement circuits, including:

(1) According to Kirchhoff's law, the characteristics of the measurement circuit of the resistive wireless passive strain sensor are analyzed, and the relationship between the resistance value of the strain sensor and the total input impedance of the circuit is established, where:

According to Kirchhoff's voltage law and the equivalent input impedance method analysis, the equivalent input impedance Z _in of the system can be obtained:

in:

j is the imaginary unit;

f is the scanning frequency;

M is the mutual inductance between the sensing inductance L ₁ and the sensing inductance L ₂ ;

is the voltage value of the input terminal voltage source;

is the current value flowing through the series resistor R1 _of the readout coil;

R ₁ is the series resistance value of the readout coil;

R ₂ is the series resistance value of the sensing coil;

R _x is the resistance value of the strain gauge;

L ₁ is the inductance value of the read coil;

L ₂ is the sensing coil inductance value;

C _p = C ₂ +C _x , that is, the sum of the resonant frequency adjustment capacitance C _x and the sensing inductance distribution capacitance C ₂ ;

At the same time, the minimum point of phase frequency f _min can be expressed by the following relational formula:

in:

f _res is the resonant frequency of the sensor;

The quality factor Q of the sensing inductance can be expressed as:

After substituting the formula on the left side of formula (2) into formula (1), the impedance phase expression at f _res can be obtained:

in:

B is an intermediate parameter, which is a parameter related to the coupling coefficient and remains unchanged. Its expression is as follows:

in:

tan(ω _res ) is the tangent value of the phase at the resonant frequency point;

R _x,0 is the resistance value of the resistance strain sensor in the initial state (that is, no strain state);

(2) Use the following multi-objective optimization model to optimize each parameter to obtain more significant system input impedance phase information:

Find A＝(L ₁ ,L ₂ ,C _x ,R ₂ , _fres )

Max Y(A)=Q(A)

st C ₂ =C ₀ (6)

R _x =R _a (7)

L _b ≤ L ₁ ≤ L _c (8)

L _d ≤ L ₂ ≤ L _e (9)

C _f ≤ C _x ≤ C _g (10)

R _h ≤ R ₂ ≤ R _i (11)

f _i ≤ f _res ≤ f _j (12)

In the formula:

The design variable A=(L ₁ , L ₂ , C _x , R ₂ , f _res ) is the description parameter of the multi-parameter constrained optimization function;

The objective function Y(A) is the quality factor, and L(A) is the change rate of the system input impedance phase with the strain resistance value, the purpose is to convert the optimization problem into a standard optimization model;

Constraint condition (6) is to set the sensing coil inductance distributed capacitance value;

The constraint condition (7) is to set the initial resistance value of the strain gauge;

Constraint condition (8) is in order to limit the minimum value and the maximum value of the inductance value of sensing coil inductance itself;

Constraint condition (9) is to limit the minimum value and the maximum value of the inductance value of the read coil inductance itself;

The constraint condition (10) is to limit the minimum value and the maximum value of the frequency adjustment capacitor;

Constraint condition (11) is in order to limit the minimum value and the maximum value of the sensing coil series resistance;

The constraint condition (12) is to limit the minimum value and the maximum value of the resonant frequency;

(3) The multi-objective optimization function gamultiobj carried in the Matlab software is used to solve the optimization model in the step (2), and the design variable A is solved;

(4) According to the design variable A obtained in step (3) and the Pareto front-end diagram generated by Matlab software, the relationship between the system input impedance phase and the frequency variation at different strain resistance values after optimization can be obtained, and it is compared with the system input before optimization The relationship between impedance phase and frequency variation is compared, and the improvement effect of using optimized parameters on the relationship between system input impedance phase and frequency variation is illustrated.

Beneficial effects: the present invention discloses an optimal design method for measuring circuits of resistive wireless passive strain sensors, which has the following beneficial effects:

Through the strain sensor measurement system designed in the present invention, under the condition that the strain resistance value variation of the strain measurement circuit and the distributed capacitance of the sensed inductance are kept unchanged, a more significant system input impedance phase change is realized within a certain frequency range, and it can also be It is understood that the readout distance of the wireless passive strain sensor is increased under the condition that the sensitivity of the readout coil is kept unchanged, so as to improve the problem that the working distance of the wireless passive strain measurement technology using electromagnetic induction is very limited.

Description of drawings

Fig. 1 is the equivalent schematic diagram of the measurement circuit of the resistive wireless passive strain sensor;

Fig. 2 is a qualitative diagram of the trend of system input impedance amplitude and phase with scanning frequency in the present invention;

Fig. 3 is the Pareto front-end figure that solution optimization model obtains in the present invention;

Fig. 4 is the comparison diagram of the relationship between the initial value and the optimized parameter in different strain resistance values and the system impedance phase angle in the 0.4MHz-3.0MHz frequency range obtained by the present invention;

Fig. 5 is a comparison diagram of the relationship between the initial value and the optimized parameter in the frequency range of 0.4MHz-3.0MHz obtained by the present invention at different strain resistance values and the change of the system S _1,1 parameter.

in:

1- sense coil

2- Sensing coil

Detailed ways:

Specific embodiments of the present invention will be described in detail below.

The invention discloses an optimal design method for a resistance wireless passive strain sensor measuring circuit to solve the problems that the wireless passive strain sensor designed using inductive coupling characteristics has a limited monitoring distance and the actual measurement effect of strain characteristic information is not significant.

According to Kirchhoff's law and the equivalent input impedance method, the present invention firstly analyzes the characteristics of the measuring circuit of the resistive wireless passive strain sensor designed with the principle of mutual inductance coupling, and deduces the relational expression between the resistance value of the strain sensor and the total input impedance of the circuit (The qualitative simulation of this relationship is shown in Figure 2). Using this relational expression, it is analyzed that there is a mapping relationship between the change of the resistance value of the strain sensor and the phase change of the total input impedance of the circuit within a certain frequency range; The impedance phase change ΔPhase _Zin at the frequency and the sensory inductance quality factor Q are maximized as the objective function, and the optimal design model for the parameters of the resistive wireless passive strain sensor measurement circuit is established; moreover, the constraint conditions are set and the multi-objective optimization solution method is used The values of each parameter after optimization are obtained; finally, the parameter values of sensory inductance self-inductance, resistance and capacitance given above are used to compare the corresponding strain characteristic information of the initial parameters and optimized parameters in the mathematical model.

An optimal design method for a resistive wireless passive strain sensor measurement circuit, comprising the following steps:

(1) Analyze the circuit characteristics of the resistive wireless passive strain sensor measurement circuit according to Kirchhoff's law, and derive the relationship between the resistance value of the strain sensor and the total input impedance of the circuit, where:

As shown in Figure 1, it is an equivalent schematic diagram of a resistive wireless passive strain sensor measurement circuit. Looking at FIG. 1 from left to right, the readout coil 1 can be represented by an equivalent circuit composed of a readout inductance L ₁ , an inductor series resistance R ₁ and a distributed capacitance C ₁ . In the same way, the sensing coil 2 can also be represented by an equivalent circuit composed of sensing inductance L ₂ , inductor series resistance R ₂ and distributed capacitance C ₂ on the right side, and the frequency tuning capacitor C _x and strain gauge resistance R _x connected in parallel with the sensing coil A resistive strain measurement sensor resonant circuit is formed, and the R _x resistance value changes with the detected strain.

According to Kirchhoff's voltage law and the equivalent input impedance method analysis, the equivalent input impedance Z _in of the system can be obtained:

in:

j is the imaginary unit;

f is the scanning frequency;

is the voltage value of the input terminal voltage source;

is the current value flowing through the series resistor R1 _of the readout coil;

R ₁ is the series resistance value of the readout coil;

R ₂ is the series resistance value of the sensing coil;

R _x is the resistance value of the strain gauge;

L ₁ is the inductance value of the read coil;

L ₂ is the sensing coil inductance value;

C _p = C ₂ +C _x , that is, the sum of the resonant frequency adjustment capacitance C _x and the sensing inductance distribution capacitance C ₂ ;

At the same time, the minimum point of phase frequency f _min can be expressed by the following relational formula:

in:

f _res is the resonant frequency of the sensor;

The quality factor Q of the sensing inductance can be expressed as:

After substituting the formula on the left side of formula (2) into formula (1), the impedance phase expression at f _res can be obtained:

in:

B is an intermediate parameter, which is a parameter related to the coupling coefficient and remains unchanged;

Equation (4) establishes the relationship between the strain gauge resistance R _x and the impedance phase measured at the resonant frequency f _res . The above derivation formula also has limitations. The formula cannot fully describe the impedance information of the entire resistance strain sensor wireless system. The formula expresses more impedance information in the frequency range near the resonant frequency f _res of the sensing sensor, so the formula is only applicable in the frequency range near the resonant frequency f _res .

From formula (4), except for the uncertain value of parameter B, all variables are defined earlier. Therefore, an indirect way to obtain B was found:

in:

tan(ω _res ) is the tangent value of the phase at the resonant frequency point;

R _x,0 is the resistance value of the resistance strain sensor in the initial state (that is, no strain state);

It can be known from formula (4) that within a certain frequency range, the system input impedance phase and sensed inductance series resistance R ₂ , tuning capacitor C _p , strain resistance value R _x , sensed inductance value L ₂ , sensed inductance value L ₁ and resonance Nonlinear function of frequency f _res . Therefore, in order to obtain more significant strain characteristic information, constrained optimization of the above parameters is required.

(2) Use the following multi-objective optimization model to optimize each parameter to obtain more significant system input impedance phase information.

Find A＝(L ₁ ,L ₂ ,C _x ,R ₂ , _fres )

Max Y(A)=Q(A)

st C ₂ =C ₀ (6)

R _x =R _a (7)

L _b ≤ L ₁ ≤ L _c (8)

L _d ≤ L ₂ ≤ L _e (9)

C _f ≤ C _x ≤ C _g (10)

R _h ≤ R ₂ ≤ R _i (11)

f _i ≤ f _res ≤ f _j (12)

In the formula:

The design variable A=(L ₁ , L ₂ , C _x , R ₂ , f _res ) is the description parameter of the multi-parameter constrained optimization function;

The objective function Y(A) is the quality factor, and L(A) is the change rate of the system input impedance phase with the strain resistance value, the purpose is to convert the optimization problem into a standard optimization model;

The constraint condition (6) is to set the sensing coil inductance distributed capacitance value;

The constraint condition (7) is to set the initial resistance value of the strain gauge;

Constraint condition (8) is in order to limit the minimum value and the maximum value of the inductance value of sensing coil inductance itself;

Constraint condition (9) is to limit the minimum value and the maximum value of the inductance value of the read coil inductance itself;

The constraint condition (10) is to limit the minimum value and the maximum value of the frequency adjustment capacitor;

Constraint condition (11) is in order to limit the minimum value and the maximum value of the sensing coil series resistance;

The constraint condition (12) is to limit the minimum value and the maximum value of the resonant frequency;

(3) The multi-objective optimization function gamultiobj carried in the Matlab software is used to solve the optimization model in the step (2), and the design variable A is solved;

(4) According to the design variable A obtained in step (3) and the Pareto front-end diagram generated by Matlab software (as shown in Figure 3), the relationship between the phase of the system input impedance and the frequency variation with different strain resistance values after optimization can be obtained, and It is compared with the relationship between the system input impedance phase and the frequency variation before the optimization, and the improvement effect of the optimized parameters on the system input impedance phase variation with the frequency is illustrated.

Advantage of the present invention can further illustrate by following mathematical model simulation:

1. Simulation parameters

As shown in Figure 1, both the read inductance and the sensing inductance are planar spiral inductance coils, assuming that the mutual inductance coupling coefficient k = 0.13985, the initial value of the strain gauge resistance is R _x = 1000Ω, and the maximum strain resistance change is 20Ω, the strain applied each time The resistance value is 5Ω, and the equivalent distributed capacitance C ₂ of the sensing inductance coil is 1.1pF.

2. Simulation content and results

In the present invention, the simulation results of input impedance phase variation with frequency of systems with different strain resistance values in the objective function formula are provided. Firstly, the optimized optimal solution obtained by the multi-objective optimization model is used. Secondly, the optimal solution is brought into the mathematical model to obtain the simulation results of the system input impedance phase changing with frequency, and the original parameters and the optimized parameters are obtained. The simulation results are compared.

It can be seen from Table 1 that in a certain frequency range, the local optimal parameters obtained by the optimization method can obtain more significant characteristics of the change of the resistance angle of the strain resistance value, as shown in Figure 4. It can be seen from the figure that the optimized The input impedance phase of the system corresponding to the initial value of the strain gauge obtained by the subsequent parameters is lower, and at the same time, the phase change is obviously larger when the total strain resistance change is fixed. In addition, it can be seen from Figure 5 that the energy transfer efficiency is also increasing while the strain characteristic information changes significantly during the mutual inductance coupling process.

Table 1 Comparison of initial parameters, optimized parameters and results

The embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above-mentioned embodiments, and various changes can be made within the scope of knowledge of those skilled in the art without departing from the gist of the present invention.

Claims (1)

1. The optimal design method for the resistive wireless passive strain sensor measuring circuit is characterized in that, comprising: (1) According to Kirchhoff's law, the characteristics of the measurement circuit of the resistive wireless passive strain sensor are analyzed, and the relationship between the resistance value of the strain sensor and the total input impedance of the circuit is established, where: According to Kirchhoff's voltage law and the equivalent input impedance method, the equivalent input impedance Z _in of the system can be obtained: in: j is the imaginary unit; f is the scanning frequency; M is the mutual inductance between the sensing inductance L ₁ and the sensing inductance L ₂ ; is the voltage value of the input terminal voltage source; is the current value flowing through the series resistor R1 _of the readout coil; R ₁ is the series resistance value of the readout coil; R ₂ is the series resistance value of the sensing coil; R _x is the resistance value of the strain gauge; L ₁ is the inductance value of the read coil; L ₂ is the sensing coil inductance value; C _p = C ₂ +C _x , that is, the sum of the resonant frequency adjustment capacitance C _x and the sensing inductance distribution capacitance C ₂ ; At the same time, the minimum point of phase frequency f _min can be expressed by the following relational formula: in: f _res is the resonant frequency of the sensor; The quality factor Q of the sensing inductance can be expressed as: After substituting the formula on the left side of formula (2) into formula (1), the impedance phase expression at f _res can be obtained: in: B is an intermediate parameter, which is a parameter related to the coupling coefficient and remains unchanged. Its expression is as follows: in: tan(ω _res ) is the tangent value of the phase at the resonant frequency point; R _x,0 is the resistance value of the resistance strain sensor in the initial state (that is, no strain state); (2) Use the following multi-objective optimization model to optimize each parameter to obtain more significant system input impedance phase information: Find A＝(L ₁ ,L ₂ ,C _x ,R ₂ , _fres ) Max Y(A)=Q(A) stC ₂ =C ₀ (6) R _x =R _a (7) L _b ≤ L ₁ ≤ L _c (8) L _d ≤ L ₂ ≤ L _e (9) C _f ≤ C _x ≤ C _g (10) R _h ≤ R ₂ ≤ R _i (11) f _i ≤ f _res ≤ f _j (12) In the formula: The design variable A=(L ₁ , L ₂ , C _x , R ₂ , f _res ) is the description parameter of the multi-parameter constrained optimization function; The objective function Y(A) is the quality factor, and L(A) is the change rate of the system input impedance phase with the strain resistance value, the purpose is to convert the optimization problem into a standard optimization model; Constraint condition (6) is to set the sensing coil inductance distributed capacitance value; The constraint condition (7) is to set the initial resistance value of the strain gauge; Constraint condition (8) is in order to limit the minimum value and the maximum value of the inductance value of sensing coil inductance itself; Constraint condition (9) is to limit the minimum value and the maximum value of the inductance value of the read coil inductance itself; The constraint condition (10) is to limit the minimum value and the maximum value of the frequency adjustment capacitor; Constraint condition (11) is in order to limit the minimum value and the maximum value of the sensing coil series resistance; The constraint condition (12) is to limit the minimum value and the maximum value of the resonant frequency; (3) The multi-objective optimization function gamultiobj carried in the Matlab software is used to solve the optimization model in the step (2), and the design variable A is solved; (4) According to the design variable A obtained in step (3) and the Pareto front-end diagram generated by Matlab software, the relationship between the system input impedance phase and the frequency variation at different strain resistance values after optimization can be obtained, and it is compared with the system input before optimization The relationship between impedance phase and frequency variation is compared, and the improvement effect of using optimized parameters on the relationship between system input impedance phase and frequency variation is illustrated.