首页|基于激光诱导击穿光谱的瞬态温度测量方法

基于激光诱导击穿光谱的瞬态温度测量方法

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  • 国家科技期刊平台
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温度是影响材料力学性能的重要因素之一,准确测量器件温度是认识材料在应力作用下其力学性能演变以及评估设备健康状态和寿命的重要方式.面向功率器件开关过程中焊接界面快速温变测量的需求,传统方法存在时间分辨能力不足、难以测量瞬态温度的问题.文中基于激光诱导元素特征谱线强度与温度的密切相关性,提出了一种微秒量级时间分辨能力的表面温度测量方法,并建立了样品表面温度与光谱特性之间的定量关系.研究结果表明,物质表面温度提升导致激光诱导等离子体光谱强度和信噪比增强,且增强效果受到光谱采集延时和门宽影响.采用反向传播-人工神经网络(back propagation-artificial neural network,BP-ANN)和偏最小二乘(partial least squares,PLS)法对表面温度与光谱特性关系定量拟合并校准,拟合模型线性相关性拟合度指标均大于0.99.BP-ANN拟合模型的拟合偏差更小,其均方根误差(root mean squared error,RMSE)为2.582,正确率为98.3％.该方法为物体瞬态温度测量提供了一种有效手段,对功率器件焊接界面健康状态的评估给予了有力支撑.
Transient temperature measurement based on laser-induced breakdown spectroscopy
Temperature plays a crucial role in influencing the mechanical properties of materials.Accurately measuring the temperature of devices is essential for understanding the evolution of their mechanical properties under stress and evaluating their health and lifespan.However,traditional methods encounter challenges in measuring transient temperatures and lack sufficient time-resolution capability,particularly when it comes to the rapid temperature changes at the solder interface during the switching process of power devices.In this paper,based on the close correlation between the intensities of the characteristic spectral lines of the laser-induced elements and the temperatures,a method of measuring the surface temperatures with the time-resolved capability of the order of microsecond is proposed,and a quantitative relationship between the surface temperatures of the sample and the spectral characteristics is established.The findings demonstrate that an increase in the surface temperature of the material results in enhanced intensity and signal-to-noise ratio of laser-induced plasma spectra.This enhancement is influenced by the spectral acquisition delay and gate width.To establish a quantitative relationship between surface temperature and spectral properties,back propagation-artificial neural network(BP-ANN)and partial least squares(PLS)are employed for fitting and calibration.The fitted models can achieve linear correlation coefficient indexes exceeding 0.99.Notably,the BP-ANN fitted model exhibites a small fitting bias,with a root mean squared error(RMSE)of 2.582 and a correctness rate of 98.3％.The method provides an effective means for transient temperature measurement of objects and gives a strong support for the assessment of the health status of the soldering interface of power devices.

Laser-induced breakdown spectroscopytemperature measurementprincipal component analysistime resolutionpartial least squares(PLS)back propagation-artificial neural network(BP-ANN)

廖文龙、李哲、杨玥坪、唐博、魏文赋

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国网四川省电力公司电力科学研究院,四川成都 610041

西南交通大学电气工程学院,四川成都 611756

激光诱导击穿光谱 温度测量 主成分分析 时间分辨 偏最小二乘(PLS) 反向传播-人工神经网络(BP-ANN)

国家自然科学基金资助项目

52077182

2024

10.12158/j.2096-3203.2024.04.021

电力工程技术
江苏省电力公司 江苏省电机工程学会

电力工程技术

CSTPCD北大核心
影响因子：0.969
ISSN：2096-3203
年,卷(期)：2024.43(4)
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