Classification and spectrum optimization method of grease based on infrared spectrum

IF 6.3 1区 工程技术 Q1 ENGINEERING, MECHANICAL Friction Pub Date : 2023-12-05 DOI:10.1007/s40544-023-0786-y
Xin Feng, Yanqiu Xia, Peiyuan Xie, Xiaohe Li
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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.

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基于红外光谱的油脂分类及光谱优化方法
利用红外光谱法获得了63种含6种不同增稠剂的润滑脂的红外吸收光谱数据。采用Kohonen神经网络算法对润滑脂类型进行识别。结果表明,该机器学习方法可以有效地消除红外光谱中的干扰条纹,完成高维光谱数据的特征选择和降维。63种润滑脂在一定的红外光谱识别光谱带下呈现空间聚类,与润滑脂的特征峰相关联,提高了润滑脂的识别精度。该模型对聚脲润滑脂、硫酸钙复合润滑脂、铝基润滑脂、膨润土润滑脂和锂基润滑脂的识别准确率分别为100.00%、96.08%、94.87%、100.00%和87.50%。根据每种润滑脂产生的不同红外吸收光谱带,绘制出润滑脂的三维空间分布图,在识别精度的同时也验证了分类的准确性。本文验证了识别速度快、准确率高,证明了Kohonen神经网络算法对润滑油的种类识别具有高效的识别能力。
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来源期刊
Friction
Friction Engineering-Mechanical Engineering
CiteScore
12.90
自引率
13.20%
发文量
324
审稿时长
13 weeks
期刊介绍: Friction is a peer-reviewed international journal for the publication of theoretical and experimental research works related to the friction, lubrication and wear. Original, high quality research papers and review articles on all aspects of tribology are welcome, including, but are not limited to, a variety of topics, such as: Friction: Origin of friction, Friction theories, New phenomena of friction, Nano-friction, Ultra-low friction, Molecular friction, Ultra-high friction, Friction at high speed, Friction at high temperature or low temperature, Friction at solid/liquid interfaces, Bio-friction, Adhesion, etc. Lubrication: Superlubricity, Green lubricants, Nano-lubrication, Boundary lubrication, Thin film lubrication, Elastohydrodynamic lubrication, Mixed lubrication, New lubricants, New additives, Gas lubrication, Solid lubrication, etc. Wear: Wear materials, Wear mechanism, Wear models, Wear in severe conditions, Wear measurement, Wear monitoring, etc. Surface Engineering: Surface texturing, Molecular films, Surface coatings, Surface modification, Bionic surfaces, etc. Basic Sciences: Tribology system, Principles of tribology, Thermodynamics of tribo-systems, Micro-fluidics, Thermal stability of tribo-systems, etc. Friction is an open access journal. It is published quarterly by Tsinghua University Press and Springer, and sponsored by the State Key Laboratory of Tribology (TsinghuaUniversity) and the Tribology Institute of Chinese Mechanical Engineering Society.
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