|
|
|
|
|
|
| Study on the Aging Behavior of Transformer Oil Based on Machine
Learning and Infrared Spectroscopy Technology |
| XIAO Zhong-liang, YUAN Rong-yao, FU Zhuang, LIU Cheng, YIN Bi-lu, XIAO Min-zhi, ZHAO Ting-ting, KUANG Yin-jie, SONG Liu-bin* |
College of Chemistry and Chemical Engineering, Changsha University of Science and Technology, Changsha 410114, China
|
|
|
|
|
Abstract To solve the problems of complexity and large errors in oil aging analysis at the present stage, a technique integrating infrared spectroscopy and machine learning is proposed. With the help of a Fourier-Transform Mid-Infrared (FT-MIR) spectrometer, the sample spectra of three kinds of transformer oils were collected at different aging times. Various preprocessing methods were used to preprocess the sample spectra, and then the peaks were automatically sought and the sum of the characteristic peak areas was obtained. PLSR and PSO-SVR were used to establish a quantitative analysis model of transformer oil aging degree, and the effects of multiple spectral data preprocessing methods on the processing effects of infrared spectral noise reduction and baseline correction, as well as on the quantitative analysis effects of two models were investigated and analyzed. The results show that the best oil spectral preprocessing is the smoothing method, in which the SG+SVR and SG+PLSR model fitting Goodness-of-Fit (R2) are 98.14% and 99.13%, respectively, and the mean absolute error (MAE) is 0.312 4 and 0.288 0, and the root-mean-square error(RMSE) is only 0.097 7 and 0.379 0. Under the appropriate preprocessing conditions, both machine learning algorithms are robust and reliable, and the difference between the predicted and actual values of the models is extremely small.
|
|
Received: 2024-01-14
Accepted: 2024-05-28
|
|
|
|
Corresponding Authors:
SONG Liu-bin
E-mail: liubinsong1981@126.com
|
|
[1] Ugochukwu E，Azam N，Arshad A, et al. Sensors，2022，22(20): 7923.
[2] Kalashnikov D A，Paterova A V，Kulik S P, et al. Nature Photonics，2016，10(2): 98.
[3] Zhang W，Kasun L C，Wang Q J, et al. Sensors，2022，22(24): 9764.
[4] CHEN Lu, CHANG Long-fei, SHEN Mu-ao, et al(陈 露，常龙飞，沈沐傲, 等). Journal of Instrumental Analysis(分析测试学报)，2024，43(3): 489.
[5] ZHANG Hai-liang, XIE Chao-yong, TIAN Peng, et al(章海亮，谢潮勇，田 彭, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2023，43(7): 2226.
[6] YANG Cheng-en, SU Ling, FENG Wei-zhi, et al(杨承恩，苏 玲，冯伟志, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2023，43(2): 577.
[7] ZHU Ya-kun, LIU Ru-can, ZHANG Er-qiang, et al(朱亚昆，刘如灿，张二强, 等). Tobaco Science & Technology(烟草科技)，2024，57(6): 7.
[8] REN Shuang-zan, ZHONG Li-sheng, YU Qin-xue, et al(任双赞，钟力生，于钦学, 等). Journal of Xi'an Jiaotong University(西安交通大学学报)，2010，44(10): 88.
[9] Abdelrahman M Alshehawy, Diaa-Eldin A Mansour，Mohsen Ghali, et al. Processes，2021，9(5): 732.
[10] Yuan B，Wang L，Liu P, et al. Genetics in Medicine，2020，22(10): 1633.
[11] Hong J H，Hwang E S，McManus M T, et al. Science，2005，309(5737): 1074.
[12] Hailing L，Chaohua Y，Linchun W, et al. Physica A: Statistical Mechanics and Its Applications, 2022, 607: 128227.
[13] Clifford C Shekter, Day Yi, Hina J Panchal, et al. Plastic and Reconstructive Surgery，2018，142(3): 493.
[14] Mohammed Ayman R, Hassan Kumail S, et al. IFAC PapersOnLine，2022，55(10): 749.
[15] Yi H，Fangcheng K，Xin W, et al. Building and Environment，2022，211: 108723.
[16] Simone P，Amanda D d M，Edoardo L, et al. Molecules，2021，26(20): 6245.
[17] Yihe W，Dave Z S. Computational Statistics & Data Analysis，2021，156: 107130.
[18] Jokin E，Daniel S-G，Laura A, et al. Chemometrics and Intelligent Laboratory Systems，2023, 240: 104883.
[19] Qian Y，Carolina B M，Eleanor S, et al. PLoS Medicine，2022，19(9): e1004090.
[20] GUO Peng, ZHAO Yang, SUN Zi-hao, et al(郭 鹏，赵 阳，孙子皓, 等). China Agricultural Informatics(中国农业信息)，2023，35(1): 55.
[21] Bing Cheng Z，Hua Min L，Shao Hui L, et al. British Journal of Anaesthesia, 2022, 129(5): e140-e142.
[22] Yizhen H，Lin H，Xiaoqing Y, et al. Food Chemistry: X, 2023, 19: 100733.
[23] Yamina S，Farid Y, Abdelhamid I. Electric Power Systems Research, 2022, 210: 108153.
[24] Chen J，Ma S, Wu Y. Journal of Ambient Intelligence and Humanized Computing, 2022, 13: 5699.
[25] Wenying Z，Huaguang Z，Jinhai L, et al. IEEE/CAA Journal of Automatica Sinica, 2017, 4(3): 520.
[26] Jie Shi，Wei Jen Lee, Yongqiang Liu, et al. IEEE Transactions on Industry Applications, 2012, 48(3): 1064.
[27] Gupta D，Borah P，Sharma U M, et al. Neural Computing and Applications, 2022, 34: 11335.
[28] Mengwei S，Prayag T，Yuqin Q, et al. Knowledge-Based Systems, 2022, 250: 109174.
[29] Eun P J，H N V，Pei-Chien T, et al. Gastroenterology, 2023, 164(6S).
[30] Mishra P，Karami A，Nordon A, et al. Sensors Actuators B: Chemical, 2019, 281: 1034.
|
| [1] |
JU Lei1, YU Jie1, WU Yan-miao2, LI Li2, LU Tian3, DING Ya-ping2, SHU Ru-xin1*. Comparative Study of Hyperspectral Preprocessing Methods and Multiple Models in Classification and Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 125-132. |
| [2] |
XU Yang1, MAO Yi-lin1, LI He1, WANG Yu1, WANG Shuang-shuang2, QIAN Wen-jun1, DING Zhao-tang2*, FAN Kai1*. Multispectral and Hyperspectral Prediction Models of REC, SPAD and MDA in Overwintered Tea Plant[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 256-263. |
| [3] |
CAO Wang1, MAO Ya-chun1*, WEN Jie1, DING Rui-bo1, XU Meng-yuan1, FU Yan-hua2. Study on Inversion Method of Anshan-Type Iron Ore Grade Based on
Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3494-3503. |
| [4] |
HUANG Lin-feng1, JIANG Xue-song1, 2*, JIA Zhi-cheng1, ZHOU Hong-ping1, 2, ZHOU Lei1, RONG Zi-fan1. Deep Learning-Based Monitoring of Nutrient Content in Pear Trees[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3543-3552. |
| [5] |
FAN Jie-jie1, 2, QIU Chun-xia1, FAN Yi-guang2, CHEN Ri-qiang2, LIU Yang2, BIAN Ming-bo2, MA Yan-peng2, YANG Fu-qin4, FENG Hai-kuan2, 3*. Wheat Yield Prediction Based on Continuous Wavelet Transform and
Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2890-2899. |
| [6] |
LI Xiang1, ZHANG Yong-bin1, LIU Ming-yue1, 2, 3, 6*, MAN Wei-dong1, 2, 3, 6, KONG De-kun4, SONG Li-jie1, SONG Jing-ru1, WANG Fu-zeng5. Comparative Analysis of Hyperspectral Estimation Models for Soil
Texture in Coastal Wetlands[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2568-2576. |
| [7] |
ZHAO En-bo1, 2, 3, SHI Ze-lin1, 2*, LIU Yun-peng1, 2, LI Chen-xi1, 2. Effect of Temperature and Light on Fluorescence Characteristics of
Mineral Transformer Oil for EMU[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2233-2239. |
| [8] |
SONG Shao-zhong1, FU Shao-yan2, LIU Yuan-yuan2, QI Chun-yan3, LI Jing-peng4, GAO Xun2*. Identification of Rice Origin Using Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1553-1558. |
| [9] |
SONG Shao-zhong1, LIU Yuan-yuan2, ZHOU Zi-yang3, TENG Xing3, LI Ji-hong3, LIU Jun-ling1, GAO Xun2*. Identification of Sorghum Breed by Hyperspectral Image Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1392-1397. |
| [10] |
LIU Zi-yang1, 2, FENG Shuai1, 2, ZHAO Dong-xue1, 2, LI Jin-peng1, 2, GUAN Qiang1, 2, XU Tong-yu1, 2*. Research on Spectral Feature Extraction and Detection Method of Rice Leaf Blast by UAV Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1457-1463. |
| [11] |
ZHOU Zhe-hai,XIONG Tao,ZHAO Shuang,ZHANG Fan,ZHU Gui-xian. Single-Cell Blood Classification Method Based on Fluorescence Optical Tweezers and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1081-1087. |
| [12] |
CHEN Pan-pan, REN Yan-min*, ZHAO Chun-jiang, LI Cun-jun, LIU Yu*. Research on the Classification of Yingde Tea Plantations Based on Time Series Sentinel-2 Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1136-1143. |
| [13] |
CHEN Jian-hong, REN Jun-yi, YANG Jia, GUO Ya-ya, QIAO Wei-dong. Study on Non-Invasive Blood Glucose Detection Technology Based on Time Frequency Domain Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 318-324. |
| [14] |
LI Yang1, 2, LI Cui-ling2, 3, WANG Xiu2, 3, FAN Peng-fei2, 3, LI Yu-kang2, ZHAI Chang-yuan1, 2, 3*. Identification of Cucumber Disease and Insect Pest Based on
Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 301-309. |
| [15] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
|
|
|
|
