As an important part of transmission systems, coatings can improve the physical properties of gear surface. It is meaningful to research the effect of coating on the transmission performance of gears. In this paper, eight-degree-of-freedom dynamic response model of helical cylindrical gear is established considering friction, and the influence of friction factors on dynamic response is explored. The tribological properties and lubrication characteristics of the coating are investigated and compared with uncoated. The transmission performances of manganese phosphate conversion coated gears are studied experimentally. The results show that the coefficients of friction of Mn–P[C] coatings are reduced by 19%, the average amplitude and root mean square of vibration acceleration are obviously decreased, and the transmission efficiency is improved. The manganese phosphate conversion coating is beneficial to the transmission performance of gears.

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.

Environmental noise and transmission components can cause significant interference in vibration signals, rendering the extraction of bearing fault features challenging in service scenarios. Traditional fault diagnosis methods rely heavily on professional domain knowledge, prior models, and signal preprocessing methods. The accuracy of fault diagnosis depends on the quality of the fault-sensitive features extracted by vibration signal preprocessing methods. An improved convolutional neural network (CNN) end-to-end intelligent fault diagnosis model based on raw vibration data (RVDCNN) is proposed. The time-domain vibration signal of the transmission bearing is converted into a continuous two-dimensional numerical matrix, and a two-dimensional CNN model is constructed through network structure optimization. The original time-domain vibration signal numerical matrix of the bearing is trained and tested to extract and learn abstract fault features of different fault types, and then the fault classification of the bearing is achieved. To verify the generalizability of the RVDCNN intelligent fault diagnosis model, it is applied to the recognition of rolling bearings in the two-speed mechanical automatic transmission of electric vehicles, achieving recognition accuracy of 99.11% for seven types of bearings.

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.