Mechanics & Industry
Volume 22, 2021
|Number of page(s)||9|
|Published online||14 April 2021|
Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, PR China
2 School of Printing, Packaging Engineering and Digital Media Technology, Xi'an University of Technology, Xi'an 710048, PR China
* e-mail: firstname.lastname@example.org
Accepted: 29 March 2021
The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessment model is proposed based on the support vector regression, and the fuzzy uncertainty is modeled to describe the random behavior under small sample. To verify the proposed model, the sample data of cylinder liner wear is applied. For best results, the particle swarm optimization (PSO) algorithm is used to optimize the model parameters. A back propagation neural network (BPNN) is employed to verify the effectiveness of the proposed model. The results show that the novel support vector regression has better prediction accuracy than other methods for cylinder wear in this paper, the proposed model can evaluate the cylinder liner wear of the ICEs effectively. The work provides a technical support for evaluating the service performance of the piston ring-cylinder liner and a reference for regular maintenance of the ships.
Key words: Wear assessment / cylinder liner / support vector regression / fuzzy uncertainty / particle swarm optimization algorithm
© J. Kang et al., Published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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