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Research Article | Open Access

Classification and spectrum optimization method of grease based on infrared spectrum

Xin FENG1,2( ), Yanqiu XIA1( ), Peiyuan XIE1, Xiaohe LI1
School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000, China
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Abstract

The infrared (IR) absorption spectral data of 63 kinds of lubricating greases containing six different types of thickeners were obtained using the IR spectroscopy. The Kohonen neural network algorithm was used to identify the type of the lubricating grease. The results show that this machine learning method can effectively eliminate the interference fringes in the IR spectrum, and complete the feature selection and dimensionality reduction of the high-dimensional spectral data. The 63 kinds of greases exhibit spatial clustering under certain IR spectrum recognition spectral bands, which are linked to characteristic peaks of lubricating greases and improve the recognition accuracy of these greases. The model achieved recognition accuracy of 100.00%, 96.08%, 94.87%, 100.00%, and 87.50% for polyurea grease, calcium sulfonate composite grease, aluminum (Al)-based grease, bentonite grease, and lithium-based grease, respectively. Based on the different IR absorption spectrum bands produced by each kind of lubricating grease, the three-dimensional spatial distribution map of the lubricating grease drawn also verifies the accuracy of classification while recognizing the accuracy. This paper demonstrates fast recognition speed and high accuracy, proving that the Kohonen neural network algorithm has an efficient recognition ability for identifying the types of the lubricating grease.

Keywords

greaseinfrared (IR) spectroscopylayered Kohonen networkspecies recognitionspectrum band optimization

References

[1]
Meng Y G, Xu J, Ma L R, Jin Z M, Prakash B, Ma T B, Wang W Z. A review of advances in tribology in 2020–2021. Friction 10(10): 1443–1595 (2022)
Crossref Google Scholar
[2]
Marian M, Almqvist A, Rosenkranz A, Fillon M. Numerical micro-texture optimization for lubricated contacts—A critical discussion. Friction 10(11): 1772–1809 (2022)
Crossref Google Scholar
[3]
Rosenkranz A, Marian M, Profito F J, Aragon N, Shah R. The use of artificial intelligence in tribology—A perspective. Lubricants 9(1): 2 (2021)
Crossref Google Scholar
[4]
Yin N, Xing Z G, He K, Zhang Z N. Tribo-informatics approaches in tribology research: A review. Friction 11(1): 1–22 (2023)
Crossref Google Scholar
[5]
Pandiyan V, Akeddar M, Prost J, Vorlaufer G, Varga M, Wasmer K. Long short-term memory based semi-supervised encoder–decoder for early prediction of failures in self-lubricating bearings. Friction 11(1): 109–124 (2023)
Crossref Google Scholar
[6]
Perčić M, Zelenika S, Mezić I. Artificial intelligence-based predictive model of nanoscale friction using experimental data. Friction 9(6): 1726–1748 (2021)
Crossref Google Scholar
[7]
Ulas M, Altay O, Gurgenc T, Özel C. A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic, kernel-based, and weighted extreme learning machine. Friction 8(6): 1102–1116 (2020)
Crossref Google Scholar
[8]
Altay O, Gurgenc T, Ulas M, Özel C. Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms. Friction 8(1): 107–114 (2020)
Crossref Google Scholar
[9]
König F, Sous C, Jacobs G. Numerical prediction of the frictional losses in sliding bearings during start–stop operation. Friction 9(3): 583–597 (2021)
Crossref Google Scholar
[10]
Gao X L, Dai K, Wang Z, Wang T T, He J B. Establishing quantitative structure tribo-ability relationship model using Bayesian regularization neural network. Friction 4(2): 105–115 (2016)
Crossref Google Scholar
[11]
Parikh H H, Gohil P P. Experimental investigation and prediction of wear behavior of cotton fiber polyester composites. Friction 5(2): 183–193 (2017)
Crossref Google Scholar
[12]
Xia Y Q, Xu D Y, Feng X, Cai M R. Identification and content prediction of lubricating oil additives based on extreme learning machine. Tribology 40(1): 97–106 (2020) (in Chinese)
Google Scholar
[13]
Xia Y Q, Wang C, Feng X. GA–BPSO hybrid optimization of middle infrared spectrum feature band selection of lubricating oil additive type identification technology. Tribology 42(1): 142–152 (2022) (in Chinese)
Google Scholar
[14]
Xia Y Q, Wang Y X, Feng X, Cai M R. Optimization efficiency of swarm intelligence search in base oil performance prediction model. Tribology 43(4): 429–438 (2023) (in Chinese)
Google Scholar
[15]
Egala R, Jagadeesh G V, Setti S G. Experimental investigation and prediction of tribological behavior of unidirectional short castor oil fiber reinforced epoxy composites. Friction 9(7): 250–272 (2021)
Crossref Google Scholar
[16]
Hasan M S, Kordijazi A, Pohatgi P K, Nosonovsky M. Triboinformatic modeling of dry friction and wear of aluminum base alloys using machine learning algorithms. Tribology International 161: 107065 (2021)
Crossref Google Scholar
[17]
Heirendt L, Liu H H T, Wang P. Aircraft landing gear greased slider bearing steady-state thermo-elastohydrodynamic concept model. Tribol Int 82: 453–463 (2015)
Crossref Google Scholar
[18]
De Laurentis N, Kadiric A, Lugt P, Cann P. The influence of bearing grease composition on friction in rolling/sliding concentrated contacts. Tribol Int 94: 624–632 (2016)
Crossref Google Scholar
[19]
Cao Z F, Xia Y Q, Ge X Y. Conductive capacity and tribological properties of several carbon materials in conductive greases. Ind Lubr Tribol 68(5): 577–585 (2016)
Crossref Google Scholar
[20]
Hu X B, Song J, Liao Z H, Liu Y H, Gao J, Menze B, Liu W Q. Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints. Friction 10(4): 560–572 (2022)
Crossref Google Scholar
[21]
Li X H, Feng X, Xia Y Q. IR spectra of grease optimization based on cuckoo search. Spectrosc Spect Anal 37(12): 3703–3708 (2017) (in Chinese)
Google Scholar
[22]
Barbin D F, de Souza Madureira Felicio A L, Sun D W, Nixdorf S L, Hirooka E Y. Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview. Food Res Int 61: 23–32 (2014)
Crossref Google Scholar
[23]
Leong Y S, Ker P J, Jamaludin M Z, Nomanbhay S M, Ismail A, Abdullah F, Looe H M, Shukri C N S M. New near-infrared absorbance peak for inhibitor content detection in transformer insulating oil. Sensor Actuat B-Chem 266: 577–582 (2018)
Crossref Google Scholar
[24]
Pasquini C. Near infrared spectroscopy: A mature analytical technique with new perspectives—A review. Anal Chim Acta 1026: 8–36 (2018)
Crossref Google Scholar
[25]
Geminale A, Grassi D, Altieri F, Serventi G, Carli C, Carrozzo F G, Sgavetti M, Orosei R, D’Aversa E, Bellucci G, et al. Removal of atmospheric features in near infrared spectra by means of principal component analysis and target transformation on Mars: I. Method. Icarus 253: 51–65 (2015)
Crossref Google Scholar
[26]
Li Z F, Xu G J, Wang J J, Du G R, Cai W S, Shao X G. Outlier detection for multivariate calibration in near infrared spectroscopic analysis by model diagnostics. Chin J Anal Chem 44(2): 305–309 (2016)
Crossref Google Scholar
[27]
Zhang G, Liu Y, Guo J F, Cheng X Q. A hierarchical search result clustering method. J Comput Res Dev 45(3): 542–547 (2008) (in Chinese)
Google Scholar
[28]
Senthil Kumar P, Manisekar K, Narayanasamy R. Experimental and prediction of abrasive wear behavior of sintered Cu–SiC composites containing graphite by using artificial neural networks. Tribol Trans 57(3): 455–471 (2014)
Crossref Google Scholar
[29]
Martin-del-Campo S, Schnabel S, Sandin F, Marklund P. Detection of particle contaminants in rolling element bearings with unsupervised acoustic emission feature learning. Tribol Int 132: 30–38 (2019)
Crossref Google Scholar
[30]
Zupan J, Novič M, Ruisánchez I. Kohonen and counterpropagation artificial neural networks in analytical chemistry. Chemometr Intell Lab 38(1): 1–23 (1997)
Crossref Google Scholar
Friction
Volume 12 Issue 6,
June 2024
Pages 1154-1164
DOI: 10.1007/s40544-023-0786-y
Cite this article:
FENG X, XIA Y, XIE P, et al. Classification and spectrum optimization method of grease based on infrared spectrum. Friction, 2024, 12(6): 1154-1164. https://doi.org/10.1007/s40544-023-0786-y

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Received: 12 March 2023
Revised: 08 May 2023
Accepted: 07 June 2023
Published: 05 December 2023
© The author(s) 2023.

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