• Spectroscopy and Spectral Analysis
  • Vol. 40, Issue 11, 3515 (2020)
You-lie JIANG*, Shi-ping ZHU, Chao TANG, Bi-yun SUN, and Liang WANG
Author Affiliations
  • College of Engineering and Technology, Southwest University, Chongqing 400716, China
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    DOI: 10.3964/j.issn.1000-0593(2020)11-3515-07 Cite this Article
    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 Copy Citation Text show less
    Preparation flowchart of accelerated thermal aging samples
    Fig. 1. Preparation flowchart of accelerated thermal aging samples
    Original spectra of 140 insulating oil samples
    Fig. 2. Original spectra of 140 insulating oil samples
    Correlation between furfural content in oil and aging time
    Fig. 3. Correlation between furfural content in oil and aging time
    The iPLS plot of original spectra
    Fig. 4. The iPLS plot of original spectra
    Contribution rates of seven principal components of NIR spectra of insulating oil samples
    Fig. 5. Contribution rates of seven principal components of NIR spectra of insulating oil samples
    Prediction results of four aging time models
    Fig. 6. Prediction results of four aging time models
    The iPLS plot of original spectra
    Fig. 7. The iPLS plot of original spectra
    Contribution rates of four principal components of NIR spectra of insulating oil samples
    Fig. 8. Contribution rates of four principal components of NIR spectra of insulating oil samples
    Prediction results of four furfural content models
    Fig. 9. Prediction results of four furfural content models
    样品组别老化时间/h糠醛含量/(mg·L-1)
    1480.227 8
    2720.271 4
    3960.533 9
    41440.818 2
    52161.066 7
    62881.178 4
    73601.444 8
    84321.771 2
    95281.850 6
    106482.021 1
    117682.880 6
    128882.957 4
    131 0323.575 1
    141 1764.192 8
    Table 1. Furfural content and aging time of insulating oil samples
    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 2. Attribution of absorption near-infrared spectral peaks of insulating oil
    BP网络学习算法隐含层神
    经元数目
    校正集预测集
    RMSECR2RMSEPR2
    L-M优化算法216.470.997 920.350.996 8
    Quasi-Newton算法216.470.997 920.350.996 8
    贝叶斯正则化的L-M算法219.370.997 123.390.995 7
    共轭梯度算法215.590.998 118.670.997 3
    弹性梯度下降法217.050.997 720.810.996 7
    梯度下降法318.750.997 323.060.995 9
    Table 3. Predicting results of correction model with different BP learning algorithms
    建模方法校正集预测集
    RMSECR2RMSEPR2
    PLSR(None)41.890.986 943.490.986 3
    PCR24.160.995 527.230.994 6
    PLSR22.840.996 122.210.996 2
    PCA-BP-ANN15.590.998 118.670.997 3
    Table 4. Predicting results of models with different correction methods
    BP网络学习算法隐含层神
    经元数目
    校正集预测集
    RMSECR2RMSEPR2
    L-M优化算法30.161 00.981 70.175 90.978 6
    Quasi-Newton算法20.171 90.979 20.172 50.979 4
    贝叶斯正则化的L-M算法20.155 00.983 10.194 00.973 7
    共轭梯度算法30.116 00.990 50.134 40.987 7
    弹性梯度下降法20.164 00.981 00.187 20.975 4
    梯度下降法20.162 70.981 30.185 40.975 9
    Table 5. Predicting results of correction models with different BP learning algorithms
    建模方法校正集预测集
    RMSECR2RMSEPR2
    PLSR(None)0.187 00.975 30.206 60.969 9
    PCR0.148 70.984 40.181 00.977 2
    PLSR0.140 10.986 20.148 70.984 6
    PCA-BP-ANN0.116 00.990 50.134 40.987 7
    Table 6. Predicting results of models with different correction methods
    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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