1. Deep learning algorithm to predict friction coefficient of matching pairs at different temperature domains based on friction sound.
- Author
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Guo, Fei, Cheng, Ganlin, Yang, Zi, Xiang, Chong, and Jia, Xiaohong
- Subjects
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MACHINE learning , *DEEP learning , *FRICTION , *SURFACE temperature , *TEMPERATURE , *SURFACE properties - Abstract
The tribological properties of matching pairs at different temperature domain is predicted through friction sound. Two deep learning algorithms respectively by virtue of the Nonlinear Auto-Regressive models with Exogenous Inputs and long short-term memory neural network are adopted to analyze the acoustic features of friction sound to predict tribological properties of polymer surface at five temperatures within a large temperature domain under different working conditions. The deep learning algorithm precisely fits the friction coefficient in accordance with the performance analysis. [Display omitted] • A real-time friction coefficient prediction method based on friction sound. • The mapping relationship between friction sound and friction coefficient with the same random nonlinear characteristics. • The prediction method was widely applicable to the prediction of friction coefficient under wide temperature range. • This method can be used for in-situ monitoring on tribological properties of mating pairs. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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