Temperature drift compensation method of pressure instrument

Abstract

The invention relates to a temperature drift compensation method for a pressure instrument, and belongs to the technical field of pressure instruments, and the method comprises the following steps: obtaining output signal values of a pressure sensor at different temperature points, and building a temperature-output signal data set; constructing a polynomial fitting model as a temperature drift compensation model, fitting a temperature coefficient through a least square method, and storing the coefficient into an instrument memory; original values of temperature and pressure are collected in real time and substituted into the model to calculate a compensated pressure value. The method also supports various strategies such as temperature sensor calibration, nonlinear error compensation, Kalman filtering noise reduction, dynamic temperature change rate compensation and neural network compensation, and has self-calibration and remote updating functions. According to the invention, the measurement precision and stability of the pressure instrument in a variable-temperature environment are significantly improved, and the method is suitable for high-precision industrial measurement and intelligent monitoring scenes of the Internet of Things.

Description

Technical Field

[0001] This invention relates to the field of pressure instrument technology, and in particular to a method for compensating for temperature drift in pressure instruments. Background Technology

[0002] Pressure gauges are widely used in industrial process control, environmental monitoring, energy management, and other fields, and their measurement accuracy directly affects the safety and reliability of the system. However, in practical applications, changes in ambient temperature can significantly affect the output performance of pressure gauges, causing measurement results to drift, a phenomenon known as "temperature drift." This drift is mainly caused by factors such as the temperature characteristics of sensor materials, the temperature drift of electronic components, and the thermal expansion and contraction of mechanical structures.

[0003] Traditional pressure gauges mostly employ hardware compensation methods, such as using temperature-coefficient matched resistor networks or analog circuits for coarse compensation. However, this method has limited accuracy, poor adaptability, and high cost. With the development of digital processing technology, some gauges have begun to use software compensation algorithms, but problems such as inaccurate models, poor real-time performance, and narrow applicability still exist.

[0004] Therefore, there is an urgent need in this field for a pressure instrument temperature drift compensation method that can achieve high precision, good real-time performance, and wide applicability, so as to improve the measurement stability and accuracy of pressure instruments in complex temperature environments.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a pressure instrument temperature drift compensation method that can achieve high precision, good real-time performance, and wide applicability.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A method for compensating for temperature drift in a pressure instrument includes the following steps:

[0009] S1: Obtain the output signal values ​​of the pressure sensor at different temperature points and establish a temperature-output signal dataset;

[0010] S2: Construct a temperature drift compensation model, which is a polynomial fitting model, and its expression is:

[0011]

[0012] Among them, P c(T) represents the compensated pressure value, P0 is the pressure measurement value at the reference temperature, T is the current temperature, and a i Let n be the temperature coefficient of the i-th term, and n be the polynomial order.

[0013] S3: Fit the temperature coefficient a using the least squares method. i This minimizes the error between the compensated pressure value and the actual pressure value.

[0014] S4: Store the fitted coefficients into the instrument's non-volatile memory;

[0015] S5: Real-time acquisition of current temperature T and raw pressure P values raw Substitute the values ​​into the compensation model for calculation, and output the compensated pressure value P. c .

[0016] Optionally, the polynomial order n can be in the range of 3 to 5, and the optimal order is determined by cross-validation to avoid overfitting.

[0017] Optionally, the acquisition of the temperature-output signal dataset includes multi-point temperature calibration in a constant temperature chamber, with no fewer than 10 temperature points covering the instrument's operating temperature range.

[0018] Optionally, it also includes calibrating the temperature sensor and correcting the temperature measurement using the following formula:

[0019] T cal =T raw +k1·(T raw -T ref )+k2·(T raw -T ref ) 2

[0020] Among them, T cal The calibrated temperature value, T raw T is the original output value of the temperature sensor. ref Here is the reference temperature, and k1 and k2 are calibration coefficients.

[0021] Optionally, compensation for nonlinear errors in the pressure sensor can also be included, using the following piecewise linearization model:

[0022]

[0023] Among them, P linear b is the linearized pressure value. j c j P represents the coefficients for each segment. j This represents the pressure value at the segment point.

[0024] Optionally, it also includes using Kalman filtering to filter the real-time acquired temperature and pressure signals to suppress noise interference, with the state equation and observation equation as follows:

[0025]

[0026] Where, x k The state vector, z, includes temperature and pressure values. k Let A be the observation vector, H be the state transition matrix, and W be the observation matrix. k and v k These are process noise and observation noise, respectively.

[0027] Optional features include self-calibration upon instrument startup, rapid calibration via built-in standard pressure and temperature sources, and updating of compensation coefficients.

[0028] Optionally, it also includes establishing a dynamic compensation model for the effect of temperature change rate on drift, with the expression as follows:

[0029]

[0030] Where ΔP is the pressure drift correction caused by the rate of temperature change, and α and β are dynamic compensation coefficients.

[0031] Optionally, it also includes temperature drift compensation through a neural network model, wherein the neural network is a three-layer feedforward network, the activation function is sigmoid, the training algorithm is backpropagation, and its output is:

[0032] P c =f NN (P raw ,T)

[0033] Among them, f NN This is a neural network mapping function.

[0034] Optionally, it also includes uploading the compensated pressure value to the monitoring system via a communication interface, and supporting remote calibration and model update functions to achieve intelligent compensation in the Internet of Things environment.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] This invention provides a temperature drift compensation method for pressure instruments. By combining a polynomial fitting model with the least squares method to fit the temperature coefficient, the accuracy of temperature drift compensation is significantly improved, making it suitable for high-precision pressure measurement scenarios. It supports multiple compensation strategies (such as temperature calibration, nonlinear compensation, dynamic compensation, neural network compensation, etc.) to adapt to different sensor characteristics and operating environments. Real-time data acquisition and model calculation are employed, combined with Kalman filtering to suppress noise, ensuring a fast and stable compensation process. Self-calibration, remote updates, and IoT communication are supported, facilitating later maintenance and model optimization, and enhancing the instrument's intelligence level. Through filtering and dynamic compensation models, the impact of temperature abrupt changes and noise on measurement results is effectively suppressed. Software algorithm compensation replaces traditional hardware compensation, reducing manufacturing costs and improving product competitiveness. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the method flow provided in an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] The purpose of this invention is to provide a pressure instrument temperature drift compensation method that can achieve high precision, good real-time performance, and wide applicability.

[0041] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] Example 1:

[0043] This embodiment provides a method for compensating for temperature drift of a pressure gauge, the flowchart of which is shown below. Figure 1 As shown, temperature drift compensation is based on polynomial fitting:

[0044] Step 1: Data Acquisition and Calibration;

[0045] The pressure instrument was placed in a constant temperature chamber, and the output signal values ​​of the pressure sensor were recorded at 10 temperature points (-10℃, 0℃, 10℃, 20℃, 30℃, 40℃, 50℃, 60℃, 70℃, and 80℃) under different pressure standard values ​​to establish a temperature-output signal dataset.

[0046] Step 2: Model building and coefficient fitting;

[0047] A fourth-order polynomial fitting model was used:

[0048] Pc(T) = P0 + a1·T + a2·T 2 +a3·T 3 +a4·T 4 ;

[0049] The coefficients obtained by fitting using the least squares method are: a1 = -0.0023, a2 = 0.00015, a3 = -0.000004, a4 ​​= 0.0000001.

[0050] Step 3: Coefficient storage and real-time compensation;

[0051] The fitted coefficients are stored in the instrument's EEPROM. In actual operation, the temperature T and the original pressure value P_raw are collected in real time and substituted into the above model to calculate the compensated pressure value Pc.

[0052] Step 4: Verify the effect;

[0053] In a variable temperature environment, the errors of the pressure measurements before and after compensation were compared with the standard pressure values. The experiment showed that the maximum error after compensation was reduced from ±1.5%FS to ±0.2%FS, significantly improving the measurement accuracy.

[0054] Step 5: Expand functionality;

[0055] The instrument supports uploading the compensated data to the monitoring system via the RS485 interface, and can receive remote commands to update the model coefficients, thus realizing intelligent operation and maintenance.

[0056] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0057] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for compensating for temperature drift in a pressure instrument, characterized in that, Includes the following steps: S1: Obtain the output signal values ​​of the pressure sensor at different temperature points and establish a temperature-output signal dataset; S2: Construct a temperature drift compensation model, which is a polynomial fitting model, and its expression is: Among them, P c (T) represents the compensated pressure value, P0 is the pressure measurement value at the reference temperature, T is the current temperature, and a i Let n be the temperature coefficient of the i-th term, and n be the polynomial order. S3: Fit the temperature coefficient a using the least squares method. i This minimizes the error between the compensated pressure value and the actual pressure value. S4: Store the fitted coefficients into the instrument's non-volatile memory; S5: Real-time acquisition of current temperature T and raw pressure P values raw Substitute the values ​​into the temperature drift compensation model for calculation, and output the compensated pressure value P. c .

2. The method for compensating for temperature drift of a pressure instrument according to claim 1, characterized in that, The polynomial order n ranges from 3 to 5, and the optimal order is determined by cross-validation to avoid overfitting.

3. The method for compensating for temperature drift of a pressure instrument according to claim 1, characterized in that, The acquisition of the temperature-output signal dataset includes multi-point temperature calibration in a constant temperature chamber, with no fewer than 10 temperature points covering the instrument's operating temperature range.

4. The method for compensating for temperature drift of a pressure instrument according to claim 1, characterized in that, This also includes calibrating the temperature sensor, correcting the temperature measurements using the following formula: T cal =T raw +k1·(T raw -T ref )+k2·(T raw -T ref ) 2 Among them, T cal The calibrated temperature value, T raw T is the original output value of the temperature sensor. ref Here is the reference temperature, and k1 and k2 are calibration coefficients.

5. The method for compensating for temperature drift of a pressure instrument according to claim 1, characterized in that, This also includes compensation for the nonlinear error of the pressure sensor, using the following piecewise linearization model: Among them, P linear b is the linearized pressure value. j c j P represents the coefficients for each segment. j This represents the pressure value at the segment point.

6. The method for compensating for temperature drift of a pressure instrument according to claim 1, characterized in that, It also includes using Kalman filtering to filter the real-time acquired temperature and pressure signals to suppress noise interference. Its state equation and observation equation are as follows: Where, x k The state vector, z, includes temperature and pressure values. k Let A be the observation vector, H be the state transition matrix, and W be the observation matrix. k and v k These are process noise and observation noise, respectively.

7. The method for compensating for temperature drift of a pressure instrument according to claim 1, characterized in that, It also includes self-calibration when the instrument starts up, rapid calibration through built-in standard pressure and temperature sources, and updating of compensation coefficients.

8. The method for compensating for temperature drift of a pressure instrument according to claim 1, characterized in that, It also includes establishing a dynamic compensation model for the effect of temperature change rate on drift, with the expression being: Where ΔP is the pressure drift correction caused by the rate of temperature change, and α and β are dynamic compensation coefficients.

9. The method for compensating for temperature drift of a pressure instrument according to claim 1, characterized in that, It also includes temperature drift compensation using a neural network model, wherein the neural network is a three-layer feedforward network with the sigmoid activation function and the backpropagation algorithm as the training algorithm, and its output is: P c =f NN (P raw ,T) Among them, f NN This is a neural network mapping function.

10. The method for compensating for temperature drift of a pressure instrument according to claim 1, characterized in that, It also includes uploading the compensated pressure value to the monitoring system via a communication interface, and supports remote calibration and model update functions to achieve intelligent compensation in the Internet of Things environment.

Images