Hydraulic dampers are widely used in tracked-vehicle suspensions but may suffer from oil leakage and temperature rise under high-frequency and high-load operating conditions. This study proposes a permanent-magnet eddy current damper driven by a planetary roller screw (RS-ECD). The planetary roller screw converts the relative linear motion of the suspension into rotary motion, while a Halbach permanent-magnet array enhances the air-gap magnetic field and electromagnetic damping torque. An analytical model and a finite-element electromagnetic model are established to investigate the mechanical and electromagnetic characteristics of the RS-ECD. The electromagnetic torque is calculated directly from the instantaneous rotor speed and coupled with a three-degree-of-freedom suspension-track dynamic model incorporating the equivalent mass of the ground-contact track segment and track-pitch-induced polygonal excitation. Under Classes D–G road conditions at 15 m/s, the RS-ECD reduces the RMS values of vehicle-body acceleration, road-wheel acceleration, suspension dynamic deflection, and dynamic wheel load by 10.3–34.6%, 29.9–66.2%, 51.3–57.5%, and 5.1–33.1%, respectively, compared with a linear hydraulic damper. Although localized peaks in vehicle-body acceleration and dynamic wheel load increase slightly under the rougher road conditions, the overall vibration intensity and suspension-stroke demand are reduced. These results demonstrate the potential of the RS-ECD for sustained vibration attenuation in tracked-vehicle suspensions while also revealing a trade-off in transient peak control.
This work analyzes elastic wave propagation in a layered structure with piezoelectric and micropolar properties. The main physical quantities in this system, mechanical displacements, normal and shearing stress, horizontal and vertical components of electric displacement, shearing couple stress components, and electric field components for the piezoelectric and micropolar media are described analytically using normal mode analysis. The obtained results demonstrate that the distribution of physical quantities though the depths of the two media is significantly influenced by the governing parameters, including the thickness of the piezoelectric layer, initial stress, and harmonic load. The relationship between each parameter and the geometry has been explicitly demonstrated. The observations demonstrate that the physical quantities increase with increasing thickness h 1 while h 2 is held constant in the medium. Additionally, the normal stress of the piezoelectric and micropolar multilayer materials decreases due to the isotropy of the medium, and the shear stress also decreases. Further, a correlation is found between the thickness of the piezoelectric layer, initial stress, and harmonic load inside the medium, and a corresponding decrease in the physical quantities. These results provide valuable insights for the design and development of novel-layered composites, high-performance surface acoustic wave (SAW) devices, smart piezoelectric sensors, and actuators for engineering applications, including sensing, signal processing in electronic systems, biomedical diagnostics, treatment of diseases, various technological and commerical fields, such as television, air conditioning systems that automatically regulate temperature, and fire alarms. The surface wave propagation is calculated numerically. The electromechanical coupling factor is calculated for various piezoelectric layer widths h 1 , initial stresses P, and harmonic loads ϕ 0 . The obtained results are verified by recovering previous results as particular cases of this investigation.
Vibration mitigation using piezoelectric shunt damping is effectively performed using a digital shunt that consists of a voltage-controlled current source and a digital control unit. Compared to fully passive circuits, the digital shunt can be retuned by updating its control transfer function; however, it loses all damping performance during an outage. The present study introduces a hybrid configuration that combines a digital shunt and a passive resonant shunt with analog components. The passive shunt can be designed to secure a certain minimum performance at power loss, while the digital shunt is used to retain optimal damping. Tuning expressions are derived for a digital unit that synthesizes an additional resonant circuit, resulting in a 2×RL hybrid shunt with two series RL-elements in parallel. The proposed hybrid analog-digital shunt thereby acts as a sensorless two-terminal device, suitable for practical engineering applications that require fail-safe behavior. The effectiveness and stability of the hybrid shunt are derived for an equivalent single-degree-of-freedom structure, and subsequently validated experimentally for a composite blade.
Existing multi-source domain methods tend to directly map raw data into a shared subspace, while neglecting explicit decoupling between fault-related and non-related information. Moreover, these methods primarily enforce domain-level alignment without introducing class-level constraints, which leads to blurred inter-class boundaries and unexcellent discrimination under domain shift. To tackle these problems, this paper proposes a decoupled feature learning framework with class-guided adversarial alignment (DFLFCAA). Firstly, a decoupled feature generator with channel-wise adaptive thresholds is designed to decompose the learned features into fault-related and non-related components. Only the former are retained for subsequent diagnostic tasks, thereby eliminating domain-specific interference at the feature level. Secondly, a class prototype contrastive alignment strategy is introduced, which constructs cross-domain class prototypes and enforces three constraints to achieve fine-grained class-level structural alignment in the shared subspace. Thirdly, a task-aware domain discriminator is developed by incorporating class prediction probabilities into the discriminator input, which enables category-conditioned domain judgment that prevents the confusion between class variance and domain variance. Extensive evaluations on multiple datasets confirm that DFLFCAA achieves superior fault identification accuracy and robustness under scarce-label scenarios.
In long-distance belt conveyor systems, idler bearings are widely distributed along the transportation route, making distributed fault diagnosis necessary. A cloud-edge architecture provides a feasible solution by enabling local inference at edge devices and collaborative model training through the cloud. However, distributed idler-bearing diagnosis still faces two major challenges: (1) heterogeneous sensor data across different idlers and time-varying fault features under evolving operating conditions and (2) limited edge computing and communication resources. To address these challenges, this study proposes Di-FedDIL, a distributed fault diagnosis framework for idler bearings under cloud-edge collaboration. The framework consists of the dilated separable attention convolution network (DiSAC-Net), federated learning via contrastive alignment and progressive Euclidean regularization (FedCAP), and dual-anchor incremental learning (DIL). For edge diagnosis, DiSAC-Net performs lightweight feature extraction and online fault inference at edge devices. For federated collaborative learning, FedCAP enables distributed clients to collaboratively learn shared diagnostic knowledge while adapting to heterogeneous local data. For incremental updating, DIL continuously adapts personalized models to evolving operating conditions while balancing new knowledge acquisition and historical knowledge retention. In addition, a hierarchical greedy optimization strategy jointly performs task offloading and bandwidth allocation to reduce diagnostic response time under cloud-edge resource constraints. Experimental results show that Di-FedDIL outperforms the compared federated learning methods. The proposed resource-allocation strategy reduces task completion time by approximately 25% compared with fully local computing. These results demonstrate that Di-FedDIL provides a practical solution for distributed idler-bearing fault diagnosis while improving execution efficiency under limited cloud-edge computing and communication resources.
