Herein, to endow h-BN nanosheets with gelling ability, a diurea compound was decorated on the h-BN nanosheets via designed adsorption and in situ reaction processes. The prepared h-BN-based gelator, BTO, exhibited excellent dispersibility in non-polar liquid media, and the gelation of BTO dispersions could be readily triggered by ultrasonic treatments. The sol–gel transformation of the system was found to be highly reversible by stirring and sonication. Based on the investigation on the self-assembly behavior of BTO nanosheets in the liquid phase, it was proposed that a continuous and layered structure formed by BTO during sonication was the key factor for the gelling properties of these nanosheets. The viscoelasticity of the sonication-induced gel was studied using a rheometer. Tribological evaluations show that the prepared h-BN nanogel exhibits outstanding lubricating performances, and more importantly, it has been proved that the gel state of the h-BN nanosheets provides superior and more reliable lubricating performances than the corresponding dispersion state under certain conditions; this can be ascribed to the formation of a continuous and uniform structure of modified h-BN nanosheets during gelation. Thus, this study not only clarifies the key role of the assembly structure in the tribological performances of 2D nanomaterials, but also demonstrates the potential of gelation in improving the macroscopic friction reduction and wear resistance of 2D nanomaterials.

In recent years, the powerful feature extraction capabilities of deep learning have attracted widespread attention in the field of bearing fault diagnosis. To address the limitations of single-modal and single-channel feature extraction methods, which often result in incomplete information representation and difficulty in obtaining high-quality fault features, this paper proposes a dual-channel parallel multimodal feature fusion model for bearing fault diagnosis. In this method, the one-dimensional vibration signals are first transformed into two-dimensional time-frequency representations using continuous wavelet transform (CWT). Subsequently, both the one-dimensional vibration signals and the two-dimensional time-frequency representations are fed simultaneously into the dual-branch parallel model. Within this architecture, the first branch employs a combination of a one-dimensional convolutional neural network (1DCNN) and a bidirectional gated recurrent unit (BiGRU) to extract temporal features from the one-dimensional vibration signals. The second branch utilizes a dilated convolutional to capture spatial time–frequency information from the CWT-derived two-dimensional time–frequency representations. The features extracted by both branches were are input into the feature fusion layer. Furthermore, to leverage fault features more comprehensively, a channel attention mechanism is embedded after the feature fusion layer. This enables the network to focus more effectively on salient features across channels while suppressing interference from redundant features, thereby enhancing the performance and accuracy of the dual-branch network. Finally, the fused fault features are passed to a softmax classifier for fault classification. Experimental results demonstrate that the proposed method achieved an average accuracy of 99.50% on the Case Western Reserve University (CWRU) bearing dataset and 97.33% on the Southeast University (SEU) bearing dataset. These results confirm that the suggested model effectively improves fault diagnosis accuracy and exhibits strong generalization capability.

In the existing literature, there are few studies on the effect of deposition bias on the tribological properties of carbon-doped high-entropy alloy coatings. In order to further study the effect of the deposition bias on the properties of coatings, (AlTiVCrNb)C_xN_y coatings were deposited via unbalanced RF magnetron sputtering. The microstructure and tribological properties of carbon-doped high-entropy alloy ceramic coatings under different deposition biases were studied. The composition, morphology, crystal structure, and chemical morphology of each element of the coating were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The hardness, elastic modulus, friction, and wear properties of the coating were further characterized using a nanoindentation instrument, reciprocating sliding friction, a wear tester, and a white light interferometer. The coating density reached the optimal level when the deposition bias value was 90 V. The hardness and elastic modulus of the (AlTiVCrNb)C_xN_y coating increased first and then decreased with an increase in deposition bias, and the maximum hardness was 23.98 GPa. When the deposition bias was 90 V, the coating formed a good-quality carbon transfer film on the surface of the counterbody due to sp2 clusters during the friction and wear process. The average friction coefficient and wear rate of the (AlTiVCrNb)C_xN_y coating were the lowest, 0.185 and 1.6 × 10⁻⁷ mm³/N·m, respectively. The microstructure, mechanical properties, and tribological performance of the (AlTiVCrNb)C_xN_y coating were greatly affected by the change in deposition bias, and an (AlTiVCrNb)C_xN_y coating with excellent structure and friction properties could be prepared using graphite co-sputtering.

Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, complicating the modeling of time dependent relationships and degradation states; therefore, a piecewise linear degradation model is appropriate. An RUL prediction method is proposed based on degradation assessment and spatiotemporal feature fusion, which extracts strongly time correlated features from bearing vibration data, evaluates sensitive indicators, constructs weighted fused degradation features, and identifies abrupt degradation points. On this basis, a piecewise linear degradation model is constructed that uses a path graph structure to represent temporal dependencies and a temporal observation window to embed temporal features. By incorporating GAT-LSTM, RUL prediction for bearings is performed. The method is validated on the XJTU-SY dataset and on a loaded ball bearing test rig for electric vehicle drive motors, yielding comprehensive vibration measurements for life prediction. The results show that the method captures deep degradation information across the full bearing life cycle and delivers accurate, robust predictions, providing guidance for the health assessment of electric drive bearings in new energy vehicles.

Accurate prediction of bearing temperature rise offers essential support for equipment operation and optimized design. However, traditional methods often lack accuracy under the complex operating conditions of new energy electric drive bearings. To address this, we propose a model–data integration-driven approach for predicting the temperature rise in new energy electric drive bearings. First, a data-driven optimization method is employed to integrate mathematical and simulation models, generating highly reliable simulation data. Then, the simulation data and measured data are fused to construct an integrated dataset for bearing temperature rise. Finally, a CNN-LSTM prediction model is established and trained using this dataset. Validation experiments were carried out on the EV6206E-2RZTN/C3 bearing to verify the effectiveness of the proposed method. Results show (1) under constant operating conditions, the MAE during the temperature rise phase is 0.773 °C, and the steady-state phase maximum MAE is 0.686 °C, and (2) under variable operating conditions, the maximum MAE during the temperature rise phase is 0.713 °C, and the steady-state phase maximum MAE is 0.764 °C. The proposed method achieves effective prediction of temperature rise in electric drive bearings and offers a valuable reference for addressing temperature prediction challenges under complex operational conditions.