|
|
|
|
|
|
Near Infrared Spectroscopy Analysis of Moisture in Engine Oil |
LIU Ge1, CHEN Bin2*, SHANG Zhi-xuan2, QUAN Yu-xuan2 |
1. Hebei Key Laboratory of Hazardous Chemicals Safety and Control Technology, School of Chemical and Environmental Engineering, North China Institute of Science and Technology, Yanjiao 065201, China
2. School of Environmental Engineering, Hebei Key Laboratory of Safety Monitoring of Mining Equipment,North China Institute of Science and Technology, Yanjiao 065201, China
|
|
|
Abstract Engine oil is the core component of the engine. It is easy to mix with water in the engine oil, which can easily accelerate the deterioration and deterioration of the engine oil, and then harms the safe operation of the engine. Detecting water in the engine oil is an important indicator to ensure the quality of the engine oil. Moisture is easy to accelerate the deterioration and degradation of engine oil, and it is harmful to the safe operation of the engine, and its detection is an important index to ensure the quality of engine oil. Therefore, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) regression method was used to detect engine oil with different water content. Firstly, the mechanism of 931, 1 195~1 212 and 1 391~1 430 nm wavelengths with strong absorption peaks were analyzed according to the NIR characteristics of water-containing engine oil. Orthogonal signal correction (OSC) and several other spectral pretreatment methods were used to construct the PLS regression model, and the characteristic wavelength was selected according to the regression coefficient. The results showed that the PLS model pretreated by OSC had the better predictive ability, while the pretreated by MSC and SNV reduced the correction ability of the model. The 166 feature wavelengths were selected, accounting for 32.42% of the spectrum. The fourteen oil samples in the prediction set were predicted using the established near-infrared full spectrum PLS model and the characteristic wavelength selected PLS model. Both models can achieve good prediction, and the standard deviation of prediction is 0.000 7 and 0.000 6, respectively. The PLS model selected by characteristic wavelength had the most robust prediction and the best performance index (R2P was 0.993 0, R2CV was 0.988 7, RMSECV and RMSEP were 3.140 1×10-4 and 2.419 0×10-4, RPD was 11.988 4). Compared with the full-spectrum model, the PLS model with characteristic wavelength selection can eliminate much useless information in the full spectrum, predict the water content of engine oil most robustly and have the best performance index so that the performance of the model has been significantly improved. The prediction set of oil samples was verified according to the established full-spectrum PLS model after OSC pretreatment and the PLS model for characteristic wavelength selection. The prediction effect of the PLS model after characteristic wavelength selection was good, and the predicted value of each oil sample was closer to the measured value. It indicates that the PLS model established after characteristic wavelength selection does not reduce the accuracy and prediction ability of the model, but eliminates the information of unrelated variables, making the model more generalized. Therefore, the near-infrared spectroscopy technology has good accuracy and reliability in detecting moisture in engine oil, which provides a feasible solution for engine condition monitoring.
|
Received: 2021-12-10
Accepted: 2022-04-03
|
|
Corresponding Authors:
CHEN Bin
E-mail: hustchb@163.com
|
|
[1] Al Sheikh Omar A, Motamen Salehi F, Farooq U, et al. Tribology International, 2021, 160(13): 107050.
[2] Kr Singh D, Kurien J, Villayamore A. Materials Today: Proceedings, 2021, 44: 3976.
[3] Zhou F, Yang K. IOP Conference Series. Materials Science and Engineering, 2021, 1043(5): 52054.
[4] Johns M L, Lisabeth K W. Measurement Science & Technology, 2016, 27(10): 105501.
[5] Abdulmunaim A M, Reuter M, Abdulmunem O M, et al. Transactions of the Asabe, 2016, 59: 795.
[6] Liu J, Han J, Zhang Y, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 204: 33.
[7] Basati Z, Jamshidi B, Rasekh M, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 203: 308.
[8] Liu C, Tang X, Yu T, et al. Optik, 2020, 224: 165694.
[9] Holland T, Abdul-Munaim A, Watson D, et al. Lubricants, 2019, 6(2): 35.
[10] CHEN Bin, LIU Ge(陈 彬, 刘 阁). High Voltage Engineering(高电压技术), 2020, 46 (4): 1405.
[11] Holzki M, Fouckhardt H, Klotzbücher T. Sensors and Actuators A: Physical, 2012, 184: 93.
[12] CHEN Bin, LIU Ge(陈 彬, 刘 阁). Acta Photonica Sinica(光子学报),2014, 43(2): 0230001-1.
[13] Rudnitskaya A, Rocha S M, Legin A, et al. Analytica Chimica Acta, 2010, 662(1): 82.
|
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[4] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[5] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[6] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
[7] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[8] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[9] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[10] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[11] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[12] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[13] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
[14] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[15] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
|
|
|
|