Nonlinear correction method of pressure sensor based on data fusion

Yin, Yue; Xu, Shi-Jiang; Zuo, Xin

doi:10.1016/j.measurement.2022.111303

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…The issue of significant data discrepancies and broad fluctuations when continuously measuring three gases for brief time spans [32]， This is because the results of the three gas concentration measurements contain the real gas information to be measured, noise signals generated by the natural rise and fall of radioactivity. [33]The random change in rate, amplitude and phase characteristics produced by noise interference leads to increased random errors and chances of errors in data processing, which results in data processing distortions and a reduction in the quality of useful data.…”

Section: Optimized Least Squares-wavelet Transform Soft Thresholding ...mentioning

confidence: 99%

A Novel Environmental pressure Compensation Approach of IR Gas Sensors in Industrial environment

Su,

Zhang,

Wang

et al. 2024

Preprint

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In order to improve the analytical accuracy and applicability of infrared gas sensors, this paper examines the need to integrate a pressure compensation algorithm into the infrared gas analyzer, identifying appropriate compensation methods and optimizing test data accuracy. First, a test platform was constructed to evaluate the accuracy of three types of infrared sensors for trace gases in industrial environments. The sensors were tested in the following order: CO2 at concentrations of 0.5%-4.5%; CH4 at concentrations of 2%-18%; and CO at 100-900ppm standard gas concentrations, found that the maximum value of the test error were 48%, 44.5%, 45%, respectively, do not meet the error range specified in the standard. Secondly, the constant-pressure accuracy test was carried out by selecting the same concentration of CO2, CH4 and CO standard gases and substituting them into the optimized least squares compensation model, and it was found that the maximum errors of the three gas tests were 16%, 9% and 24%, which were superior to the standard error ranges. Thirdly, the pressure compensation was verified for the three infrared gas analyzers after algorithmic compensation within the pressure compensation device. Based on this, the optimized least squares-wavelet transform soft threshold coupling model were established to integrate the ambient pressure, the test concentration and the standard gas concentration with the optimization model, and it was found that the maximum measurement errors after optimization with the optimized least squares-wavelet transform soft thresholding coupling method were 0.2%, 0.05% and 1%. In summary, this paper develops an error optimization model for various environmental pressure conditions, and provides a theoretical framework for the research and development of infrared gas analyzers and other spectroscopic gas detecting instruments. Additionally, it establishes a technical foundation for constructing in-situ on-line monitoring systems for trace gases in industrial environments.

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Section: Optimized Least Squares-wavelet Transform Soft Thresholding ...mentioning

confidence: 99%

A Novel Environmental pressure Compensation Approach of IR Gas Sensors in Industrial environment

Su,

Zhang,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Simpson et al applied a linear correction to align the measured values with a certified and traceable reference temperature meter. Regular calibration of thermometric devices improves the accuracy of aging instruments and ensures that their sensitivity and specificity are retained in diagnosing various diseases and pathologies [17]. Wu et al proposed a Pt100 temperature sensor calibration method based on linear regression analysis to further improve the conversion accuracy of the temperature sensor program and increase stability and reliability [18].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Calibration of Temperature Sensors

Liu,

Zhao,

Wang

et al. 2023

Sensors

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Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.

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Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Gated Recurrent Unit

Liu,

Wang,

Jiang

et al. 2024

Sensors

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Piezoresistive pressure sensors have broad applications but often face accuracy challenges due to temperature-induced drift. Traditional compensation methods based on discrete data, such as polynomial interpolation, support vector machine (SVM), and artificial neural network (ANN), overlook the thermal hysteresis, resulting in lower accuracy. Considering the sequence-dependent nature of temperature drift, we propose the RF-IWOA-GRU temperature compensation model. Random forest (RF) is used to interpolate missing values in continuous data. A combination of gated recurrent unit (GRU) networks and an improved whale optimization algorithm (IWOA) is employed for temperature compensation. This model leverages the memory capability of GRU and the optimization efficiency of the IWOA to enhance the accuracy and stability of the pressure sensors. To validate the compensation method, experiments were designed under continuous variations in temperature and actual pressure. The experimental results show that the compensation capability of the proposed RF-IWOA-GRU model significantly outperforms that of traditional methods. After compensation, the standard deviation of pressure decreased from 10.18 kPa to 1.14 kPa, and the mean absolute error and root mean squared error were reduced by 75.10% and 76.15%, respectively.

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Nonlinear correction method of pressure sensor based on data fusion

Cited by 3 publications

References 53 publications

A Novel Environmental pressure Compensation Approach of IR Gas Sensors in Industrial environment

A Novel Environmental pressure Compensation Approach of IR Gas Sensors in Industrial environment

Machine-Learning-Based Calibration of Temperature Sensors

Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Gated Recurrent Unit

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