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A signal processing method based on friction noise is introduced to predict the tribological properties of polymers in a wide temperature domain. Three different machine learning algorithms, XGBoost, LightGBM and CatBoost are used to establish a mapping relationship between the time–frequency domain features of friction noise and the friction coefficient. The friction coefficients of pairs of four polymers and seven metals are predicted at five temperatures and three load/speed conditions of point contacts in a wide temperature domain from − 120–25 ℃. Performance analysis reveals that the machine learning approach satisfactorily predicts the friction coefficients of different polymer–metal pairs in a wide temperature range from tribological test data. This method can be used for in-situ monitoring on tribological properties. [Display omitted] • A real-time friction coefficient prediction method based on friction noise was developed by using machine learning algorithm. • The following mapping relationship between friction noise and friction coefficient was found. • The prediction method was widely applicable to the prediction of friction coefficient. • This method can be used for in-situ monitoring on tribological properties of mating pairs. [ABSTRACT FROM AUTHOR]

Establishing a correlation between the friction coefficient and friction noise of metal-friction polymer interfaces is a challenging task in various environments. To address this issue, our study utilizes machine learning algorithms to construct a friction data-based model, elucidating the relationship between noise and friction coefficient. We propose the variational mode decomposition (VMD) along with five machine learning algorithms, each capturing unique data characteristics. Algorithm optimization is achieved through the implementation of L1 and L2 regularization methods. By comparing the regression results, we develop a tribological model that offers a novel approach for constructing a nonlinear model, enabling precise monitoring of the friction coefficient by leveraging noise signals. [ABSTRACT FROM AUTHOR]