TY - JOUR
T1 - Gaussian Process Regression for Quantitative Degree of Polymerization Analysis of Oil-Paper Insulation by EPO-NIRS
T2 - A Solution to Field Moisture
AU - Li, Yuan
AU - Li, Han
AU - Zhang, Wenbo
AU - Wang, Chen
AU - Zhang, Guanjun
N1 - Publisher Copyright:
© 1972-2012 IEEE.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - In recent years, the near infrared (NIR) spectroscopy has been used in prediction of the degree of polymerization (DP) of oil-paper insulation, but the practices show that the field moisture has a significant influence on the acquired spectra. In this paper, an EPO-GPR model is proposed to eliminate the interference of external moisture and hence to improve the prediction performance of DP of insulating paper in the laboratory as well as on-site experiments. In this model, external parameter orthogonalization (EPO) extracts the differences between the insulating paper samples with varying moisture contents and builds a projection matrix orthogonal to the moisture spectral information; the Gaussian process regression (GPR) with optimized Matérn kernel is employed to mapping the correlation between the spectra and DP of insulating paper. Compared with classical PLS and BPNN models, the EPO-GPR model achieves high prediction accuracy in the laboratory testing (RMSE of 71) and field experiments (RMSE of ∼ 60). The proposed EPO-GPR model can provide a solution to field moisture when using the NIR spectroscopy technique to on-site assess the aging condition of insulating papers.
AB - In recent years, the near infrared (NIR) spectroscopy has been used in prediction of the degree of polymerization (DP) of oil-paper insulation, but the practices show that the field moisture has a significant influence on the acquired spectra. In this paper, an EPO-GPR model is proposed to eliminate the interference of external moisture and hence to improve the prediction performance of DP of insulating paper in the laboratory as well as on-site experiments. In this model, external parameter orthogonalization (EPO) extracts the differences between the insulating paper samples with varying moisture contents and builds a projection matrix orthogonal to the moisture spectral information; the Gaussian process regression (GPR) with optimized Matérn kernel is employed to mapping the correlation between the spectra and DP of insulating paper. Compared with classical PLS and BPNN models, the EPO-GPR model achieves high prediction accuracy in the laboratory testing (RMSE of 71) and field experiments (RMSE of ∼ 60). The proposed EPO-GPR model can provide a solution to field moisture when using the NIR spectroscopy technique to on-site assess the aging condition of insulating papers.
KW - Aging condition
KW - external parameter orthogonalization
KW - gaussian process regression
KW - insulating paper
KW - near infrared spectroscopy
UR - https://www.scopus.com/pages/publications/85147218502
U2 - 10.1109/TIA.2023.3234508
DO - 10.1109/TIA.2023.3234508
M3 - 文章
AN - SCOPUS:85147218502
SN - 0093-9994
VL - 59
SP - 2750
EP - 2759
JO - IEEE Transactions on Industry Applications
JF - IEEE Transactions on Industry Applications
IS - 3
ER -