Sliding bearings are critical components of the internal combustion engine. Friction and wear occur in the contact area between the shaft and the bearing. Significant wear can occur in poor working conditions or after a long service time, leading to the failure of the sliding bearing and affecting the reliability of the machinery. It is essential to investigate the wear performance of sliding bearings, understand their wear mechanism, predict their service life, and select wear-resistant materials and surface treatments. This paper reviews the current status and prospects of sliding bearing wear research, focusing on the classification of sliding bearing wear tests, wear testing machines, wear test research, wear prediction models, and future research prospects.

Lubricating oil can effectively reduce friction between mechanical parts, thereby reducing energy consumption and improving service life and reliability. Due to the development of science and technology, it is necessary to improve the performance of lubricating oil to fulfill the higher tribological requirements for countering wear and providing lubrication. Nanolubricant additives have the four lubrication mechanisms of micro-bearing, protective film, polishing, and repair effects. A nanolubricant additive can often demonstrate a variety of lubrication mechanisms at the same time. As lubricating additives, metal and metal oxide nanoparticles have outstanding effects which improve the tribological properties of lubricating oil and have been widely studied in the field of tribology. This paper introduces the lubrication mechanism of nanoadditives and the latest research results for metal and metal-oxide nanoparticle lubrication additives.

The performance of the marine sliding bearing affects the reliability of marine engineering directly. Nevertheless, sliding bearings are subjected to frictional forces and problems, such as wear and fatigue, that occur after long-term use, which leads to failure. In this study, a sliding bearing friction calculation program is developed using the Fortran language, which calculates the friction force of the bearing under different working conditions. Simultaneously, a component-level sliding bearing tribology testing machine is designed, which predicts the wear failure and explores the wear mechanism for different types of sliding bearings. The model calculation results are in good agreement with the experimental data, which verifies the model’s correctness. Through the wear test, the wear mechanism of the sliding bearing is mainly scratches, and the electroplating layer in the groove is squeezed out of it and attached to the surface of the aluminum alloy to reduce friction. The model and the testing machine provide theoretical guidance for the friction durability of sliding bearings used in marine engineering and guarantee the reliability of marine engineering.

To predict the mass loss of grooved journal-bearing shells under varying operating conditions while reducing experimental effort, accelerated wear tests were conducted on a component-level journal-bearing test rig. The effects of load, rotational speed, and oil temperature were investigated, and the worn surfaces were characterized microscopically to elucidate the wear mechanism. A backpropagation (BP) neural network was trained on 42 mass-loss samples and evaluated using 18 additional samples. Candidate numbers of hidden-layer neurons were identified using an empirical relation and compared based on prediction performance. A genetic algorithm (GA) and the sparrow search algorithm (SSA) were then used to optimize the initial weights and biases of the BP network. Mass loss increased with load and oil temperature but decreased with rotational speed; all responses were nonlinear. After testing, the groove structure remained visible, while the lead-based overlay became thinner and underwent plastic flow. Pb and Sn were detected on the aluminum-alloy surface, suggesting transfer of overlay material during sliding. The mean absolute percentage error (MAPE) and coefficient of determination (R²) were 26.986% and 0.54552, respectively, for the conventional BP model; 8.436% and 0.9862 for the GA-BP model; and 2.6296% and 0.99714 for the SSA-BP model. For the present dataset, the SSA-BP model achieved lower prediction errors than the BP and GA-BP models and reproduced the variation in mass loss across the investigated operating conditions.