Research on the surface temperature compensation model of rotary kiln based on polynomial fitting and piecewise correction function

Qian, Shengsheng; Dai, Shaosheng; Chen, Yamei; Tan, Wei; Hu, Ang; He, Peiliang

doi:10.1016/j.infrared.2018.08.010

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…Researchers made efforts on reduction of sensing errors caused by these interferences. Some algorithms were developed by previous researchers [29], such as least squares [30], polynomial fitting [31], and interpolation [32], etc., but these methods do not reflect real-time output data and cannot be used for real-time monitoring tasks. This disadvantage limits their usage scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

Tang

Gao

et al. 2021

IEEE Access

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Due to the instability of the characteristics of materials, fabrication processes and user handling, newly designed and fabricated wireless passive surface acoustic wave (WP-SAW) sensor nodes have inconsistent sensing performance. Furthermore, ambient environmental interferences aggravate inconsistences under complex working conditions. In this paper, a multi-iteration enhanced two-point simple moving average (MI-2P-SMA) method is proposed for sensing error reduction of a WP-SAW reflective delay line water temperature and pressure sensor. This method is improved from the traditional 2P-SMA method for better performance on error reduction. The results show: the MI-2P-SMA method does not change the original characteristics of experimental data; it can reduce relative errors of the WP-SAW reflective delay line water temperature and pressure sensor and has better performance than a traditional 2P-SMA method; it reduces the number of data points and the extent of this reduction is dependent on iteration time.

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Section: Introductionmentioning

confidence: 99%

A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

Tang

Gao

et al. 2021

IEEE Access

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“…Software compensations were conducted with the help of an external temperature sensor and mathematic algorithms [ 16 ], which can be classified into four main types: Least square [ 17 , 18 ], polynomial fitting [ 19 , 20 ], support vector machine [ 21 ] and back-propagation (BP) neural network [ 22 , 23 ]. The approach of least square is widely used to describe the linear relationship between variables and is compatible with curve fitting.…”

Section: Introductionmentioning

confidence: 99%

A Resonant Pressure Microsensor with Temperature Compensation Method Based on Differential Outputs and a Temperature Sensor

Xiang

Yan

et al. 2020

Micromachines

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This paper presents the analysis and characterization of a resonant pressure microsensor, which employs a temperature compensation method based on differential outputs and a temperature sensor. Leveraging a silicon-on-insulator (SOI) wafer, this microsensor mainly consists of a pressure-sensitive diagram and two resonant beams (electromagnetic driving and electromagnetic induction) to produce a differential output. The resonators were vacuum packaged with a silicon-on-glass (SOG) cap using anodic bonding and the wire interconnection was realized by sputtering an Au film on highly topographic surfaces using a hard mask. After the fabrication of the resonant pressure microsensor, systematic experiments demonstrated that the pressure sensitivity of the presented microsensor was about 0.33 kPa/Hz. Utilizing the differential frequency of the two resonators and the signal from a temperature sensor to replace the two-frequency signals by polynomial fitting, the temperature compensation method based on differential outputs aims to increase the surface fitting accuracy of these microsensors which have turnover points. Employing the proposed compensation approach in this study, the errors were less than 0.02% FS of the full pressure scale (a temperature range of −40 to 85 °C and a pressure range of 200 kPa to 2000 kPa).

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Development of Mathematical Model to Predict Mass Attenuation Coefficient of Silicate Glasses By using Polynomial Curve Fitting

Abughazaleh,

Fakhouri,

Sayyed

2024

Silicon

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Research on the surface temperature compensation model of rotary kiln based on polynomial fitting and piecewise correction function

Cited by 5 publications

References 3 publications

A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

A Resonant Pressure Microsensor with Temperature Compensation Method Based on Differential Outputs and a Temperature Sensor

Development of Mathematical Model to Predict Mass Attenuation Coefficient of Silicate Glasses By using Polynomial Curve Fitting

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