Fast Prediction Method of Thermal Aging Time and Furfural Content of Insulating Oil Based on Near-Infrared Spectroscopy

样品组别	老化时间/h	糠醛含量/(mg·L^-1)
1	48	0.227 8
2	72	0.271 4
3	96	0.533 9
4	144	0.818 2
5	216	1.066 7
6	288	1.178 4
7	360	1.444 8
8	432	1.771 2
9	528	1.850 6
10	648	2.021 1
11	768	2.880 6
12	888	2.957 4
13	1 032	3.575 1
14	1 176	4.192 8

Table 2. Attribution of absorption near-infrared spectral peaks of insulating oil

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Table 2. Attribution of absorption near-infrared spectral peaks of insulating oil

吸收峰位/cm^-1	归属
8 373	甲基C—H伸缩振动的二级倍频
8 264	亚甲基C—H伸缩振动的二级倍频
5 855	甲基C—H伸缩振动的一级倍频
5 799	亚甲基C—H对称伸缩振动和反对称伸缩振动的组合频
5678	亚甲基C—H伸缩振动的一级倍频
7 181, 7 076双峰	亚甲基伸缩振动和弯曲振动的组合频

Table 3. Predicting results of correction model with different BP learning algorithms

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Table 3. Predicting results of correction model with different BP learning algorithms

BP网络学习算法	隐含层神经元数目	校正集		预测集
BP网络学习算法	隐含层神经元数目	RMSEC	R²	RMSEP	R²
L-M优化算法	2	16.47	0.997 9	20.35	0.996 8
Quasi-Newton算法	2	16.47	0.997 9	20.35	0.996 8
贝叶斯正则化的L-M算法	2	19.37	0.997 1	23.39	0.995 7
共轭梯度算法	2	15.59	0.998 1	18.67	0.997 3
弹性梯度下降法	2	17.05	0.997 7	20.81	0.996 7
梯度下降法	3	18.75	0.997 3	23.06	0.995 9

Table 4. Predicting results of models with different correction methods
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Table 4. Predicting results of models with different correction methods
建模方法校正集预测集
RMSEC R² RMSEP R²
PLSR(None) 41.89 0.986 9 43.49 0.986 3
PCR 24.16 0.995 5 27.23 0.994 6
PLSR 22.84 0.996 1 22.21 0.996 2
PCA-BP-ANN 15.59 0.998 1 18.67 0.997 3

Table 5. Predicting results of correction models with different BP learning algorithms

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Table 5. Predicting results of correction models with different BP learning algorithms

BP网络学习算法	隐含层神经元数目	校正集		预测集
BP网络学习算法	隐含层神经元数目	RMSEC	R²	RMSEP	R²
L-M优化算法	3	0.161 0	0.981 7	0.175 9	0.978 6
Quasi-Newton算法	2	0.171 9	0.979 2	0.172 5	0.979 4
贝叶斯正则化的L-M算法	2	0.155 0	0.983 1	0.194 0	0.973 7
共轭梯度算法	3	0.116 0	0.990 5	0.134 4	0.987 7
弹性梯度下降法	2	0.164 0	0.981 0	0.187 2	0.975 4
梯度下降法	2	0.162 7	0.981 3	0.185 4	0.975 9

Table 6. Predicting results of models with different correction methods
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Table 6. Predicting results of models with different correction methods
建模方法校正集预测集
RMSEC R² RMSEP R²
PLSR(None) 0.187 0 0.975 3 0.206 6 0.969 9
PCR 0.148 7 0.984 4 0.181 0 0.977 2
PLSR 0.140 1 0.986 2 0.148 7 0.984 6
PCA-BP-ANN 0.116 0 0.990 5 0.134 4 0.987 7

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You-lie JIANG, Shi-ping ZHU, Chao TANG, Bi-yun SUN, Liang WANG. Fast Prediction Method of Thermal Aging Time and Furfural Content of Insulating Oil Based on Near-Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2020, 40(11): 3515

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Paper Information

Category: Research Articles

Received: Sep. 23, 2019

Accepted: --

Published Online: Jun. 18, 2021

The Author Email: JIANG You-lie (jyl95me@126.com)

DOI:10.3964/j.issn.1000-0593(2020)11-3515-07

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Furfural content and aging time of insulating oil samples

Table 1. Furfural content and aging time of insulating oil samples

Table 2. Attribution of absorption near-infrared spectral peaks of insulating oil

Table 2. Attribution of absorption near-infrared spectral peaks of insulating oil

Table 3. Predicting results of correction model with different BP learning algorithms

Table 3. Predicting results of correction model with different BP learning algorithms

Table 4. Predicting results of models with different correction methods

Table 4. Predicting results of models with different correction methods

Table 5. Predicting results of correction models with different BP learning algorithms

Table 5. Predicting results of correction models with different BP learning algorithms

Table 6. Predicting results of models with different correction methods

Table 6. Predicting results of models with different correction methods

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