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Friction 2024, 12(6): 1154-1164 https://doi.org/10.1007/s40544-023-0786-y
Research Article | Open Access | Issue | Published: 05 December 2023

Classification and spectrum optimization method of grease based on infrared spectrum

Show Author's Information 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
Keywords:
grease, infrared (IR) spectroscopy, species recognition, layered Kohonen network, spectrum band optimization
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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Abstract Full text About this article

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.


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About this article

Classification and spectrum optimization method of grease based on infrared spectrum

Show Author's information 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

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: grease, infrared (IR) spectroscopy, species recognition, layered Kohonen network, spectrum band optimization

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Publication history

Received: 12 March 2023
Revised: 08 May 2023
Accepted: 07 June 2023
Published: 05 December 2023
Issue date: June 2024

Copyright

© The author(s) 2023.

Acknowledgements

The authors would like to appreciate the financial support extended for this academic work by the Beijing Natural Science Foundation (Grant No. 2232066) and the Open Project Foundation of State Key Laboratory of Solid Lubrication (Grant No. LSL-2212)

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