Multi-spectral temperature measurement based on adaptive emissivity model under high temperature background

Chen, Liwei; Gao, Shan; Zhao, Chunhui; Wang, Chao; Sun, Zhenya; Jiang, Jing; Zhang, Zezhan; Yu, Peifeng

doi:10.1016/j.infrared.2020.103523

Cited by 27 publications

(9 citation statements)

References 9 publications

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“…The variable (wavelength) that can make the Taylor series approximate most effectively is defined as the optimal expansion wavelength (OEW). To verify the applicability of the method under different spectral emissivities, multiple spectral emissivity models [3] in Figure 1 are applied to the simulation. Gaussian functions with central wavelengths of 450 nm, 540 nm, and 630 nm are used as the spectral response curves of each channel, and the approximation effect of the Taylor series under different spectral emissivities is obtained, as shown in Figure 2.…”

Section: Icmmap-2023mentioning

confidence: 99%

“…It is achieved by capturing and calculating the thermal radiation signals emitted by the thermal target. Based on the method of acquisition, current temperature measurement methods can be primarily divided into two types: multiwavelength temperature measurement methods [2,3] and multiband temperature measurement methods [4,5]. Their applicability is also determined by the method of reception.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Three-channel wide-spectrum temperature measurement technique

Yu,

Gu,

Dong

et al. 2023

J. Phys.: Conf. Ser.

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A theoretical and experimental study of a three-channel wide-spectrum temperature measurement technique is reported. The correctness of the application of the Taylor series to the temperature measurement integral equations is verified theoretically, and the effect of spectral emissivity on the approximation of the Taylor series is discussed. In the simulation results, the insensitivity of the optimal expansion wavelength to the spectral emissivity is revealed and applied as an important basis in the temperature solution method. Temperature measurement experiments using the two-color method and three-channel wide spectrum method were carried out. The theoretical correctness of the three-channel wide-spectrum temperature measurement method and the spatial solution ability of the temperature field are verified.

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Section: Icmmap-2023mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-channel wide-spectrum temperature measurement technique

Yu,

Gu,

Dong

et al. 2023

J. Phys.: Conf. Ser.

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“…A cross-validation method was introduced to automatically determine the emissivity model, and a statistical evaluation of temperature measurement accuracy based on this method was performed by Taunay and Choueiri, 2020. Based on the back-propagation neural network, an adaptive emissivity model was introduced by Chen et al in multi-spectral temperature measurement system for true temperature estimation, where the maximum error is only 8 K (Chen et al, 2020). Other multi-spectral algorithms have also been reported in related literatures recently (Liang et al, 2018; Liu and Zheng, 2020; Sun et al, 2020; Xing et al, 2016; 2017; 2020).…”

Section: Introductionmentioning

confidence: 99%

Measurement performance evaluation and noise compensation for a Fourier transform infrared–based pyrometer

Zhang

et al. 2023

Transactions of the Institute of Measurement and Control

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The Fourier transform infrared (FTIR) spectrometer is widely used for spectrum analysis and radiation intensity measurement. In this work, a pyrometer with the temperature measurement range of 800°C to 1200°C is developed based on the FTIR spectrometer, and the calibration of the instrument coefficients using a standard radiation source is performed. On this basis, the readout noise and shot noise in the spectral signal are analyzed, and a detailed quantitative evaluation of the influences of two kinds of noise on the temperature measurement accuracy is also carried out in this work. Experimental results show that the temperature deviation caused by the two kinds of noise is less than 1°C when the wavelength is within the range of 2.0 to 5.4 μm. However, when the wavelength is greater than 5.4 μm, the maximum temperature deviation caused by the readout noise and shot noise can reach about 12°C and 18°C, respectively. In view of this, a Kalman filter method is introduced in this work to reduce temperature fluctuations caused by these noises, where the maximum fluctuation can be reduced from 83.9°C to 6.1°C with this filter method. These experimental results can provide a valuable guidance for the general-purpose calibration and assessment of the FTIR-based pyrometer.

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“…established an infrared radiation modeling method based on radial basis function (RBF) neural network, and estimated the spectral emissivity of the target. An adaptive multiwavelength radiation temperature measurement data processing method was proposed 22 . In this method, BP neural network is first used to classify emissivity model and then genetic algorithm is used to obtain the temperature.…”

Section: Introductionmentioning

confidence: 99%

“…An adaptive multiwavelength radiation temperature measurement data processing method was proposed. 22 In this method, BP neural network is first used to classify emissivity model and then genetic algorithm is used to obtain the temperature. Two-step optimization is required in this method, so it is difficult to obtain fast temperature measurement speed.…”

Section: Introductionmentioning

confidence: 99%

Generalized inverse matrix-recurrent neural network data processing algorithm for multiwavelength pyrometer

2022

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A generalized inverse matrix-recurrent neural network (GIM-RNN) data processing algorithm for unknown emissivity was proposed to measure high-temperature by multiwavelength pyrometer (MWP). First, emissivity classification was realized quickly according to a solution from generalized inverse algorithm to underdetermined multiwavelength equation group. According to the relationship between the emissivity of 2 adjacent channels of the 6 channel thermometer used in this paper, 243 emissivity models (1 Ã 3 Ã 3 Ã 3 Ã 3 Ã 3) were designed for classification. Twelve of them were shown on the schematic diagram in the text. To make the figure clear and easy to observe, the prediction results of six common emissivity models were listed as the experimental results. Then, it was input into the corresponding RNN subnetwork to inverse temperature precisely. Simulation results showed that in the range of 1500 to 3000 K temperature, the relative error of the test set was within 1.0% for the network trained after classification by GIM, whereas the relative error of the test set was within 1.2% for the network trained without GIM classification. After 5.0% random noise was added to the inputting data, the relative error still was controlled within 1.5%, which reflected the good antinoise performance of the algorithm. Multispectral measurement data of rocket engine plumes is processed by the proposed algorithm in this manuscript. The inversion results are consistent with the theoretical results. It is indicated that the proposed algorithm has good adaptability to different materials. It is expected to become a general data processing algorithm for MWP.

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Multi-spectral temperature measurement based on adaptive emissivity model under high temperature background

Cited by 27 publications

References 9 publications

Three-channel wide-spectrum temperature measurement technique

Three-channel wide-spectrum temperature measurement technique

Measurement performance evaluation and noise compensation for a Fourier transform infrared–based pyrometer

Generalized inverse matrix-recurrent neural network data processing algorithm for multiwavelength pyrometer

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