Classification and spectrum optimization method of grease based on infrared spectrum

作     者：Xin FENG Yanqiu XIA Peiyuan XIE Xiaohe LI Xin FENG;Yanqiu XIA;Peiyuan XIE;Xiaohe LI

作者机构：School of Energy Power and Mechanical EngineeringNorth China Electric Power UniversityBeijing 102206China State Key Laboratory of Solid LubricationLanzhou Institute of Chemical PhysicsChinese Academy of SciencesLanzhou 730000China 

出 版 物：《Friction》 (摩擦（英文版）)

年 卷 期：2024年第12卷第6期

页      面：1154-1164页

核心收录：

学科分类：08[工学] 082201[工学-制浆造纸工程] 0802[工学-机械工程] 0822[工学-轻工技术与工程] 

基　　金：the financial support extended for this academic work by the Beijing Natural Science Foundation(Grant No.2232066) the Open Project Foundation of State Key Laboratory of Solid Lubrication(Grant No.LSL-2212)。 

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

摘      要：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.