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Detecting lubricating oil components through a new clustering method based on sample data

Guijun Wang (School of Mathematics Sciences, Tianjin Normal University, Tianjin, China)
Guoying Zhang (School of Mathematics Sciences, Tianjin Normal University, Tianjin, China)

Industrial Lubrication and Tribology

ISSN: 0036-8792

Article publication date: 9 April 2018

85

Abstract

Purpose

This paper aims to overcome the defect that the traditional clustering method is excessively dependent on initial clustering radius and also provide new technical measures for detecting the component content of lubricating oil based on the fuzzy neural system model.

Design/methodology/approach

According to the layers model of the fuzzy neural system model for the given sample data pair, the new clustering method can be implemented, and through the fuzzy system model, the detection method for the selected oil samples is given. By applying this method, the composition contents of 30 kinds of oil samples in lubricating oil are checked, and the actual composition contents of oil samples are compared.

Findings

Through the detection of 21 mineral elements in 30 oil samples, it can be known that the four mineral elements such as Zn, P, Ca and Mg have largest contribution rate to the lubricating oil, and they can be regarded as the main factors for classification of lubricating oil. The results show that the fuzzy system to be established based on sample data clustering has better performance in detection lubricant component content.

Originality/value

In spite of lots of methods for detecting the component of lubricating oil at the present, there is still no detection of the component of lubricating oil through clustering method based on sample data pair. The new nearest clustering method is proposed in this paper, and it can be more effectively used to detect the content of lubricating oil.

Keywords

Acknowledgements

This work has been supported by National Natural Science Foundation China (Grant No. 61374009).

Citation

Wang, G. and Zhang, G. (2018), "Detecting lubricating oil components through a new clustering method based on sample data", Industrial Lubrication and Tribology, Vol. 70 No. 3, pp. 552-559. https://doi.org/10.1108/ILT-09-2016-0226

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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