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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摘要针对TDLAS高温测量时,吸收光谱容易受到探测器和硬件电路高频噪声干扰,进而影响温度测量准确度和探测灵敏度等问题,提出了一种基于主成分分析的去噪方法。首先将原始吸光度列向量进行矩阵化排列;然后通过主成分分析将其分解为主成分矩阵和得分矩阵;取合适的主成分数,利用截取部分主成分矩阵和得分矩阵重构原始数据,其中截留的主成分代表原始数据主要信息,而剔除的部分仅包含噪声信息。实验结果表明该方法用于TDLAS测量水蒸气温度,噪声去除率达81%,温度解算标准差从8.9降至5.3。

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	常海涛
	蔡静
	温悦
	朱玉梅

关键词 ：计量学, TDLAS, 燃气测温, 双线测温, 主成分分析, 去噪

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

收稿日期: 2021-09-23 发布日期: 2022-10-14

PACS:

TB942

作者简介: 常海涛(1982-),男,河北唐山人,航空工业北京长城计量测试技术研究所工程师,研究方向为光谱法测温。Email: changhaitao2005@126.com

引用本文:

常海涛,蔡静,温悦,朱玉梅. 一种基于主成分分析的TDLAS高频噪声滤波[J]. 计量学报, 2022, 43(10): 1279-1284.
CHANG Hai-tao,CAI Jing,WEN Yue,ZHU Yu-mei. A Principal Component Analysis Method for TDLAS to Remove High Frequency Noise. Acta Metrologica Sinica, 2022, 43(10): 1279-1284.

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