Hybrid Smart Temperature Compensation System for Piezoresistive 3D Stress Sensors

Kayed, Mohammed O.; Balbola, Amr A.; Lou, Edmond; Moussa, Walied A.

doi:10.1109/jsen.2020.3005091

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…In this study, a smart temperature compensation system, developed by the authors' group [31], is utilized in the experimental evaluation of the proposed sensor. Figure 7 shows the specific steps of creating the smart calibration algorithm using ANNs.…”

Section: Hybrid Smart Calibrationmentioning

confidence: 99%

“…PRCs) of piezoresistors which may affect the sensor's accuracy if not compensated. The current work utilized a hybrid smart temperature compensation system, developed by the authors [31]. In this paper, the authors used a new hybrid temperature compensation proposed by the authors' group, to smartly compensate the temperature effect on both resistance and sensitivity of the PR element.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of MEMS-based piezoresistive 3D stress/strain sensor using strain technology and smart temperature compensation

Kayed

Balbola

Lou

et al. 2021

J. Micromech. Microeng.

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This paper presents the microfabrication and testing of a membrane-free eight-element single-polarity (n-type) sensing rosette integrated with strained silicon technology over (111) silicon plane to measure the full 3D stress/strain tensor with full temperature compensation. Such n-type piezoresistive (PR) sensor has low sensitivity to the out-of-plane components compared to the in-plane components. To improve the sensitivity of such sensors to the out-of-plane components, a strained silicon technique was integrated into the sensing rosette during the microfabrication process using a highly compressive film produced by plasma enhanced chemical vapor deposition silicon nitride. For experimental verification, a prototype device featuring the proposed sensing rosette was microfabricated using semiconductors fabrication processes. The experimental analysis applied both, in-plane and out-of-plane stresses at different temperatures over a range from −20 °С to 60 °С. In this work, a smart sensing calibration algorithm, utilizing machine learning, is employed to reduce the temperature impact on both sensitivity and resistance of PR coefficients during stress measurement. The developed sensor is capable of accurately extracting the applied stress/strain components with temperature compensation.

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Section: Hybrid Smart Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of MEMS-based piezoresistive 3D stress/strain sensor using strain technology and smart temperature compensation

Kayed

Balbola

Lou

et al. 2021

J. Micromech. Microeng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then, based on the calibration dataset, additional compensation algorithms are used to correct the sensor output, such as various numerical calculation methods: look-up table algorithm [13], polynomial interpolation [14], and the hybrid compensation method [15]. Over the past few decades, researchers have concentrated on developing more efficient compensation algorithms, including some machine learning techniques [16][17][18], such as artificial neural networks, support vector machines (SVMs), and extreme learning machines (ELMs). They also used meta-heuristic algorithms [19,20] and ensemble learning methods [21] for temperature and nonlinearity compensation.…”

Section: Introductionmentioning

confidence: 99%

Accurate Nonlinearity and Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Data Generation

Zou

Jin

et al. 2023

Sensors

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Piezoresistive pressure sensors exhibit inherent nonlinearity and sensitivity to ambient temperature, requiring multidimensional compensation to achieve accurate measurements. However, recent studies on software compensation mainly focused on developing advanced and intricate algorithms while neglecting the importance of calibration data and the limitation of computing resources. This paper aims to present a novel compensation method which generates more data by learning the calibration process of pressure sensors and uses a larger dataset instead of more complex models to improve the compensation effect. This method is performed by the proposed aquila optimizer optimized mixed polynomial kernel extreme learning machine (AO-MPKELM) algorithm. We conducted a detailed calibration experiment to assess the quality of the generated data and evaluate the performance of the proposed method through ablation analysis. The results demonstrate a high level of consistency between the generated and real data, with a maximum voltage deviation of only 0.71 millivolts. When using a bilinear interpolation algorithm for compensation, extra generated data can help reduce measurement errors by 78.95%, ultimately achieving 0.03% full-scale (FS) accuracy. These findings prove the proposed method is valid for high-accuracy measurements and has superior engineering applicability.

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“…The literature [21] shows that the BP neural network temperature compensation method is effective, reliable, and robust, and can be extended to similar sensors. One study [22] shows that artificial neural networks can be applied for temperature compensation in 3D stress sensors. Another study [23] illustrates the application of genetic neural networks to sensor array compensation.…”

Section: Introductionmentioning

confidence: 99%

Research on Temperature Compensation of Multi-Channel Pressure Scanner Based on an Improved Cuckoo Search Optimizing a BP Neural Network

et al. 2022

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A multi-channel pressure scanner is an essential tool for measuring and acquiring various pressure parameters in aerospace applications. It is important to note, however, that the pressure sensor of each of these channels will drift significantly with the increase in the temperature range of the pressure measurement, and the output voltage of each of these channels will show nonlinear characteristics, which will constrain the improvements in the accuracy of the measurement. In the regression fitting process, it is difficult to fit nonlinear data with the traditional least-squares method, which leaves pressure measurement accuracy unsatisfactory. A temperature compensation method based on an improved cuckoo search optimizing a BP neural network for a multi-channel pressure scanner is proposed in this paper to improve pressure measurement accuracy in a wide temperature range. Using the chaotic simplex algorithm, we first improved the cuckoo search algorithm, then optimized the connection weights and thresholds of the BP neural network, and finally constructed an experimental calibration system to investigate the temperature compensation of the multi-channel pressure scanning valves in the −40 °C to 60 °C temperature range. The compensation test results show that the algorithm has a better compensation effect and is more suitable for the temperature compensation of multi-channel pressure scanners than the traditional least-squares method and the standard RBF and BP neural networks. The maximum full-scale error of all 32 channels is 0.02% FS (full-scale error) and below, which realizes its high-accuracy multi-point pressure measurement in a wide temperature range.

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Hybrid Smart Temperature Compensation System for Piezoresistive 3D Stress Sensors

Cited by 11 publications

References 30 publications

Development of MEMS-based piezoresistive 3D stress/strain sensor using strain technology and smart temperature compensation

Development of MEMS-based piezoresistive 3D stress/strain sensor using strain technology and smart temperature compensation

Accurate Nonlinearity and Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Data Generation

Research on Temperature Compensation of Multi-Channel Pressure Scanner Based on an Improved Cuckoo Search Optimizing a BP Neural Network

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