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Publishing Language: Chinese

Multi-source information fitting regression integrated model of coefficient of friction

Yue SUN, Ke HE, Zhinan ZHANG( )
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract

Real-time monitoring of the friction coefficient of the moving parts of a machine system is a challenging problem. The development of intelligent perception and data technology provides the possibility to use tribological correlation information to predict the friction coefficient. This paper uses multi-source friction information such as sound during the friction and wear test to form a time-sectioned friction information data set, establishes a K-fold cross-validation double-stacked regression integration model, defines the evaluation indicators for scope evaluation, and the model was tested with a variety of load test data. The results showed that the model can effectively refine the correlation characteristics of friction information, so as to accurately fit and predict the friction coefficient, and has universality for data under different load conditions.

Keywords

feature extractionregression modelcoefficient of friction (COF)triboinformaticsstacking method
CLC number: TH117.1 Document code: A Article ID: 1000-0054(2022)12-1980-09

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Journal of Tsinghua University (Science and Technology)
Volume 62 Issue 12,
December 2022
Pages 1980-1988
DOI: 10.16511/j.cnki.qhdxxb.2022.25.048
Cite this article:
SUN Y, HE K, ZHANG Z. Multi-source information fitting regression integrated model of coefficient of friction. Journal of Tsinghua University (Science and Technology), 2022, 62(12): 1980-1988. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.048

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Received: 15 November 2021
Published: 15 December 2022
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