一种基于主成分分析的TDLAS高频噪声滤波

doi:10.3969/j.issn.1000-1158.2022.10.07

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Abstract Detection of gas temperature with tunable diode laser absorption spectroscopy (TDLAS) is easily affected by the high-frequency noise of detector and hardware circuit. The principal component analysis method commonly used in spectral image denoising is applied to the high frequency noise elimination of absorbance column vector data, and a denoising method based on principal component analysis is proposed. Firstly, the original absorbance column vector is arranged in a matrix. Then it is decomposed into the main component matrix and the score matrix through principal component analysis. The appropriate principal component score is taken, and the intercepted part of the principal component matrix and score matrix are used to reconstruct the original data. The intercepted principal component represents the main information of the original data, while the eliminated part only contains noise information. Experimental results show that this method is used for TDLAS to measure water vapor temperature, and the noise removal rate is 81% and the standard deviation of single temperature calculation is reduced from 8.9 to 5.3.

Key words： metrology TDLAS gas temperature measurement two-line thermometry principle component analysis denoising

Received: 23 September 2021 Published: 14 October 2022

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TB942

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	Articles by authors
	CHANG Hai-tao
	CAI Jing
	WEN Yue
	ZHU Yu-mei

Cite this article:

CHANG Hai-tao,CAI Jing,WEN Yue, et al. A Principal Component Analysis Method for TDLAS to Remove High Frequency Noise[J]. Acta Metrologica Sinica, 2022, 43(10): 1279-1284.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.10.07 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I10/1279

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