Yujun Xue

Motor bearings are key transmission components in drive motors and they play an important role in the operational safety and stability of electric vehicles (EVs). The contact stiffness and rotational speed are key factors governing the frequency and vibration responses of bearing systems. Identifying these two parameters provides critical information for a better characterization of the dynamic behavior of bearings. In this study, we developed an efficient hybrid parameter identification method to estimate rotational speed and contact stiffness. An EV motor-bearing test rig with wireless data transmission was established, along with a four-degree-of-freedom dynamic bearing model. The rotational speed was estimated from vibration signals by time-frequency ridge tracking without a physical tachometer. The evolution of the effective equivalent contact stiffness was inversely identified by scaling the Hertzian stiffness coefficient using the artificial fish swarm algorithm. The proposed method is a potential technique for sequential updating of rotational speed and contact stiffness, which is suitable for assisting in the construction of a virtual–physical parameter-identification framework for bearing systems.

To enhance the wear resistance and load-bearing capacity of WS₂ coatings, this paper employs unbalanced magnetron sputtering technology to fabricate HfO₂/WS₂ composite coatings by regulating the deposition pressure (0.6–1.4 Pa), leveraging the superior properties of HfO₂. The microstructure, mechanical properties, and tribological behavior across a wide temperature range (room temperature to 450 °C) are systematically investigated. The results demonstrate that deposition pressure significantly modulates the coating structure and properties. At a deposition pressure of 0.6 Pa, a pronounced secondary bombardment effect leads to coarse surface particles, a thickness of only 1.525 μm, and a high hardness of 9.332 GPa, but inferior tribological performance with an average friction coefficient of 0.703. When the deposition pressure is increased to 1.4 Pa, the secondary bombardment effect weakens, resulting in an increased coating thickness of 2.125 μm, a decreased hardness of 3.88 GPa, and a significantly improved friction coefficient of 0.072. At an optimal deposition pressure of 1.0 Pa, the sputtered atoms possess moderate energy and optimal surface mobility, promoting the formation of a dense structure. The coating demonstrates a synergistic balance between mechanical load-bearing capability (hardness: 6.38 GPa) and a highly crystalline WS₂ structure, yielding superior frictional behavior characterized by a mean coefficient of friction (COF) of merely 0.062. High-temperature tribological evaluations indicate that the COF displays a non-monotonic trend, declining at first before ascending as the temperature elevates. A minimum value of 0.015 is reached at 300 °C, corresponding to a wear rate of 1.127 × 10⁻⁸ mm³·N⁻¹·m⁻¹. At 450 °C, partial oxidation of WS₂ to WO₃ causes the friction coefficient to rise to 0.045, accompanied by fluctuations. Microstructural analysis confirms that HfO₂ doping effectively suppresses the oxidation of WS₂ at elevated temperatures and promotes the preferred growth orientation of the WS₂(002) plane, thereby synergistically optimizing the wide-temperature-range lubrication performance of the coating. This study provides a novel technical approach for the design of lubricating coatings intended for high-temperature and harsh operating conditions, such as those encountered in aero-engine bearings.

The sliding shoe bearing serves as a critical rotary support component in large grinding mills. The deformation of the hollow shaft under operating conditions is a pivotal factor governing the uniformity and stability of the lubricating oil film thickness in sliding shoe bearings. To address this, a finite element model of the sliding shoe bearing system, comprising the lubricating oil film and hollow shaft, was established based on fluid–structure interaction (FSI). The model’s predictions for oil cavity pressure and hollow shaft radial displacement were validated using a custom-built test rig designed for single-shoe sliding shoe bearing oil pressure measurements. Utilizing this finite element model, the relationship between hollow shaft deformation and oil film pressure distribution was systematically investigated. The study analyzed the effects of key parameters—specifically the area ratio of the primary and secondary oil chambers, radial load, secondary oil chamber supply pressure, and primary oil chamber supply orifice diameter—on the axial and circumferential deformation of the hollow shaft. The results indicate that the oil film pressure distribution directly influences the deformation of the hollow shaft. The area ratio of the oil chambers emerges as the dominant factor affecting this deformation. Furthermore, radial load exerts a significant impact, whereas the influence of the secondary oil chamber supply pressure is relatively minor. Conversely, the inner diameter of the primary oil chamber supply orifice exhibits a negligible effect on the hollow shaft deformation.

To address the problems of insufficient information in single-modality data, difficulties in extracting high-quality fault features, poor generalization capability, and low diagnostic accuracy in rolling bearing fault diagnosis models, a novel Butterfly Optimization Algorithm-enhanced Squeeze-and-Excitation Temporal Convolutional Network (BOA\_SETCN) based multimodal fusion method for rolling bearing fault diagnosis is proposed in this paper. First, the raw vibration data was preprocessed where Continuous Wavelet Transform (CWT) was applied for performing time-frequency analysis on the original one-dimensional vibration signal, with corresponding two-dimensional time-frequency maps being generated. A multimodal fusion approach was adopted in this paper, with both the original one-dimensional vibration signals and two-dimensional time-frequency maps being utilized as dual-input modalities for the proposed BOA\_SETCN model. Initially, one-dimensional convolutional layers were employed by the framework to extract preliminary features from input signals. Subsequently, deep temporal features across multiple time steps were extracted by the Temporal Convolutional Network (TCN) architecture, which incorporates causal convolutions, dilated convolutions, and residual connections. A Squeeze-and-Excitation (SE) block was then introduced for the adaptive recalibration of channel-wise feature weights, thereby enhancing the feature representation capability. Finally, the Butterfly Optimization Algorithm (BOA) was implemented to optimize critical TCN hyperparameters, including filter numbers, kernel sizes, and stride lengths. Experimental results demonstrated 99.44\% accuracy on the Case Western Reserve University (CWRU) bearing dataset and 95.98\% accuracy on the Southeast University (SEU) bearing dataset. The experimental results prove that the proposed model achieves higher accuracy compared to other methods, effectively improves fault diagnosis accuracy, and successfully completes the classification and identification of bearing fault types.

Electromagnetic flowmeters face significant challenges in measuring complex fluids, characterized by weak flow signals and severe noise interference. Conventional solutions, such as dual-frequency rectangular wave excitation, suffer from multiple drawbacks including rich harmonic components, high electromagnetic noise during switching transitions, a propensity for resonance which shortens stabilization time, reduced sampling windows, and complex circuit implementation. Similarly, traditional single-frequency excitation struggles to balance zero stability with the suppression of slurry noise. To address these limitations, this paper proposes a novel converter design based on dual-frequency sinusoidal wave excitation. A pure hardware circuit is used to generate the composite excitation signal, which superimposes low-frequency and high-frequency components. This approach eliminates the need for a master control chip in signal generation, thereby reducing both circuit complexity and computational resource allocation. The signal processing chain employs a technique of “high-order Butterworth separation filtering combined with synchronous demodulation,” effectively suppressing power frequency, orthogonal, and in-phase interference, achieving an improvement in interference rejection by approximately three orders of magnitude (1000×). Experimental results show that the proposed converter featured simplified circuitry, achieved a measurement accuracy of class 0.5, and validated the overall feasibility of the scheme.

Due to the large mass, high end load, and long action distance of a mill relining manipulator, gravity effects inevitably lead to a reduction in end effector positioning accuracy. To solve this problem, an online calculation method is proposed to realize real-time end effector deformation prediction. First, a manipulator is simplified into two cantilever beams: the upper arm and the forearm. Second, a reaction force and moment transformation model is established based on the coupling relationship between the forearm and upper arm. Third, finite element (FE) static analysis and simulation are carried out to obtain the end deformation. A total of 3528 discrete joint configurations are selected to cover the entire joint space, and their corresponding FE solutions are used to establish the end deformation offline dataset. Finally, an online deformation calculation algorithm based on Kolmogorov–Arnold networks (KANs) is developed to predict end deformation in any working condition. Visualization analysis and validation experiments are conducted and demonstrate the superiority of the proposed method in reducing gravity effects and improving computational efficiency. In summary, the proposed method provides support for end position compensation, especially for heavy-duty manipulators.

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.

A mill relining manipulator is key maintenance equipment for liners exchanged and operated by workers inside a grinding mill. To improve the operation efficiency and safety, real-time path planning and end deformation compensation should be performed prior to actual execution. This paper proposes a five-dimensional digital twin framework to realize virtual–real interaction between a physical manipulator and virtual model. First, a real-time digital twin scene is established based on OpenGL. The involved technologies include scene rendering, a camera system, the light design, model importation, joint control, and data transmission. Next, different solving methods are introduced into the service space for relining tasks, including a kinematics model, collision detection, path planning, and end deformation compensation. Finally, a user application is developed to realize real-time condition monitoring and simulation analysis visualization. Through comparison experiments, the superiority of the proposed path planning algorithm is demonstrated. In the case of a long-distance relining task, the planning time and path length of the proposed algorithm are 1.7 s and 15,299 mm, respectively. For motion smoothness, the joint change curve exhibits no abrupt variation. In addition, the experimental results between original and modified end trajectories further verified the effectiveness and feasibility of the proposed end effector compensation method. This study can also be extended to other heavy-duty manipulators to realize intelligent automation.

WS₂ coating, as a solid lubricating material, plays a significant role in the lubrication of rotating components in spacecraft. During the launch process, however, spacecraft are exposed to high-temperature and humid atmospheric environments, which can lead to oxidative failure in the coating, thereby limiting its engineering applications. By doping with B elements, B/WS₂ was successfully prepared as a composite coating. The results demonstrate that the fabricated coating exhibits excellent high-temperature tribological performance in atmospheric environments. The mechanism through which B doping improves the high-temperature friction and wear properties of the WS₂ composite coating was revealed through high-temperature friction and wear tests. With the incorporation of B elements, the average friction coefficient of the coating was 0.071, and the wear rate was 7.63 × 10⁻⁷ mm³·N⁻¹·m⁻¹, with the wear mechanisms identified as abrasive wear and spalling. Due to high-temperature oxidation, thermal decomposition effects, and the formation of WB₄ during sputtering, the wear resistance and anti-plastic deformation capability of the coating were further improved. Compared to room-temperature test conditions, the B/WS₂ composite coating at different high temperatures exhibited superior friction coefficients and wear rates. Notably, at 150 °C, the average friction coefficient was as low as 0.015, and the wear forms were abrasive wear and adhesive wear.