Application of a new clustering algorithm to analyze FT-IR spectrum data of lubricating oils

3rd International Conference on Energy Engineering and Environmental Protection

能源学;生态环境科学

Li, Jing^1 ; Tian, Hongxiang^1 ; Ming, Tingfeng^1 ; Sun, Yunling^1 ; Zhang, Shuai^1

College of Power Engineering, Naval University of Engineering, China^1

关键词: Baseline correction;    Data normalization;    FT-IR spectrum;    FTIR spectroscopy;    IR spectral data;    Low dimensional;    New clustering algorithms;    Self-organizing feature map networks;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/227/6/062029/pdf
DOI  :  10.1088/1755-1315/227/6/062029

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Aiming at monitoring contaminated oil samples of marine engines, the FT-IR spectroscopy is used to analysis the oil samples. Under the condition of laboratory, 18 oil samples with different concentrations and different types of contaminants were measured. The types of contaminants were water, fuel dilution, ethylene glycol and oxidation. The FT-IR spectral data of oil samples is obtained. Firstly, the original FT-IR spectral data are pre-processed by the baseline correction and the data normalization. Then, the dimensions of FT-IR spectral data pre-processed are reduced by the principal component analysis (PCA) and the four different kinds of contaminant oil samples are shown by figures. Lastly, the low dimensional data are clustered by the self-organizing feature map network and the clustered results which stand for different kinds of contaminant oil samples are demonstrated in the digital form. The results showed that the accuracy of oxidation samples clustering reached 100%, the accuracy of contaminant water samples clustering reached 83%, and the accuracy of fuel or ethylene glycol contaminant samples clustering were unsatisfactory.