In this study, the tapered roller bearing was adopted to explore the effect of the manganese phosphate coating on the friction, wear, and lifespan of bearings. Line friction samples with and without a manganese phosphate coating were prepared. The White Light Interferometry was used to analyze the three-dimensional morphology and roughness, and the Optimal Schwing–Reib–Verschleiss-5 multifunctional friction and wear tester was applied to obtain the tribological parameters before and after the application of the manganese phosphate coating. A scanning electron microscopy and energy dispersive spectrometry were used to characterize the microstructure and element composition of the worn surfaces of the manganese phosphate coating. The influence of the manganese phosphate coating on the contact stress was contrasted and examined by establishing a finite element model of the tapered roller bearing. Finally, a life test of the tapered roller bearings was performed before and after the application of the manganese phosphate coating. The results demonstrate that manganese phosphate coating enhances wear resistance, lowers bearing contact stress, and reduces the coefficient of friction. The fatigue life of the tapered roller bearing can be significantly extended with a manganese phosphate coating. This is a new method that can be used to improve the service life of bearings.

Being a key component of the transmission system, the needle bearing’s performance and service life affects the overall service life of mechanical equipment. This study takes needle bearings composed of AISI 52100 steel as the research object and studies the effect of carbonitriding surface strengthening treatment on the bearing friction, wear, and fatigue life. The carbon and nitrogen co-infiltration surface-strengthening method was employed to prepare cylindrical and disc samples. The surface hardness, residual austenite content, microscopic morphology and organization composition, coefficient of friction, and wear scar were studied to analyze the effect on the wear performance of the material. The bearing fatigue wear comparison test was conducted on a test bench to compare the actual fatigue life and surface damage of the needle bearing through conventional martensitic quenching heat treatment and carbonitriding treatment. The results demonstrate that the carbonitriding strengthening method enhances the toughness of the material while improving its surface hardness. It also improves the wear resistance of the needle roller bearings, and the fatigue life of the bearings is significantly improved. In conclusion, carbon and nitrogen co-infiltration treatment is a strengthening method that effectively extends the service life of needle roller bearings, indicating its high practical value.

In modern industrial systems, diagnosing faults in the rolling bearings of high-speed rotating machinery remains a considerable challenge due to the scarcity of reliable fault samples and the inherent complexity of the diagnostic task. To address these limitations, this study proposes an intelligent fault diagnosis method that integrates a generative adversarial network (GAN) with a convolutional block attention mechanism (CBAM). First, after systematically evaluating several loss functions, a GAN based on the Wasserstein distance loss function was adopted to generate high-quality synthetic vibration samples, effectively augmenting the training dataset. Subsequently, a convolutional block attention mechanism-based convolutional neural network (CBAM-CNN) was developed. By adaptively emphasizing salient features through channel and spatial attention modules, the CBAM-CNN improves feature extraction and recognition performance under limited-sample conditions. To validate the proposed method, an experimental platform for a two-speed automatic mechanical transmission (2AMT) of an electric vehicle was developed, and diagnostic experiments were conducted on high-speed rolling bearings. The results indicate that, under extremely severe conditions, CBAM-CNN achieves a diagnostic accuracy of 96.64% for rolling element pitting defects using only 10% of authentic samples. For composite faults, the model maintains an average accuracy above 97%, demonstrating strong generalization capability. These findings provide solid theoretical support and practical engineering guidance for rolling bearing fault diagnosis under few-shot conditions.