摘要
配制了含不同颗粒污染等级的变压器油样,利用中红外光谱扫描获得油样的红外光谱数据,再采用连续投影算法提取油样红外光谱的有效波长变量,分别应用偏最小二乘法和支持向量机法方法建立了颗粒污染等级与中红外光谱有效波长的模型.所配置的油样红外光谱经过连续投影算法提取的有效波长具有特定颗粒污染物特征波长的特点,所建两种模型的预测效果均优于全谱的偏最小二乘法和支持向量机法模型,对验证集样本数据预测的决定系数分别为0.892 9、0.934 3,均方根误差为6.372×10-3、3.07×10-3,获得了较好的预测效果,为变压器油中颗粒物的检测提供了借鉴.
The different particle pollution degree in transformer oil samples were made up, the infrared spectrum data of the oil samples were acquired by using the infrared spectrum scanning. Using the successive proiections algorithm, the effective wavelength variables of the oil samples were extracted. Based on the extracted wavelength variables, two models of both the effective wavelength of the infrared spectrum and the particle contamination pollution degree were established by using partial least squares and support vector machine method. The effective wavelength of successive projections algorithm extracted from the infrared spectrum of transformer oil samples has the characteristics of the wavelength of specific particle contamination, and the prediction effects of the models are better than partial least squares model and support vector machine model using the full infrared spectrum data of the oil samples. Besides, the determination coefficient of the prediction set of oil samples are 0. 892 9, 0. 934 3 respectively with the two models, and the root mean square error are6.372×10^-3、3.07×10^-3 respectively, thereby the satisfactory prediction results has achieved, these provide a reference for the detection of the particle contamination in transformer oil.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2016年第5期168-172,共5页
Acta Photonica Sinica
基金
国家自然科学基金(No.51375516)
重庆基础与前沿研究项目(Nos.cstc2014jcyjA90015
cstc2013jcyjA90021)资助~~
关键词
污染物检测
光谱分析
连续投影算法
偏最小二乘法
支持向量机
变压器油
颗粒污染物
Pollutant detection
Spectrum analysis
Successive Projections Algorithm
Partial LeastSquares
Support Vector Machine
Transformer oil
Particulate pollutant
