Abstract

This study proposes a model-based method for real-time monitoring of the lubrication regimes in dynamically loaded radial sliding bearings using Physics-Informed Neural Networks (PINN). The proposed method replaces computationally intensive elastohydrodynamic lubrication (EHD) simulations with a PINN-based surrogate model. The model predicts hydrodynamic pressure and lubricant film-thickness distributions with comparable accuracy under dynamically varying operating conditions, enabling reliable assessment of lubrication regimes. The proposed model advances the state of the art in physics-informed modelling of mixed lubrication by extending existing approaches to simultaneously account for mixed-friction regimes through the Greenwood–Tripp contact model, transient operating conditions, and bearing surface deformation. Using only the bearing load and shaft rotational speed as inputs, the resulting hydrodynamic pressure field and corresponding lubricant film thickness can be monitored, enabling the direct assessment of the lubrication regime and potential wear risk. The proposed method is applied to a validated EHD model of a 30 mm sliding bearing test rig, where EHD simulation results are used to train, validate, and evaluate the model. The proposed framework achieved an average lubricant film-thickness prediction error of 2.34% and lubrication-regime classification errors of 7.8% and 8.2% for the static and dynamic validation cases, respectively. Furthermore, the computation time for the complete 18-time-step load case was reduced from approximately 35 h to 61.2 ms.

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