What's new

Reliable operation of auxiliary equipment is critical for hydropower stations. However, data-driven diagnosis faces the “cold start” challenge due to fault sample scarcity in high-maintenance environments. We propose a few-shot diagnostic model, Siamese Cross-Attention Network (Scan-Net), combined with a transfer learning strategy. To extract discriminative features from limited data, we utilize Multi-Scale Synchrosqueezed Wavelet Transform (MSWT) for physically consistent time-frequency representations. Unlike simple concatenation, we design a dual-stream Siamese network with a bidirectional Cross-Attention mechanism that enables explicit inter-sensor feature interaction. We introduce a Deep Mutual Learning (DML) strategy with symmetric KL divergence constraints to align prediction distributions between dual branches, serving as self-supervised regularization to prevent overfitting. We establish a transfer pathway from public datasets to field equipment. Experiments show that Scan-Net achieves 96.50 % accuracy on the CWRU dataset under the 10-shot setting, and 94.43 % average accuracy in cross-load transfer. Pilot deployment at a large-scale hydropower station provides preliminary validation, with the system contributing to a reduction in routine inspection workload

Slope stability in seismically active areas is a crucial issue in geotechnical design, as failure can have severe consequences for infrastructure and public safety. This study explores modern methods for calculating slope stability under seismic loads, including the limit equilibrium method (LEM), the finite element method (FEM), and the difference element method (DEM). Numerical modeling was conducted using the PLAXIS, GeoStudio, and Slide software packages. A parametric slope stability analysis was performed, considering various values of seismic acceleration, slope angle, and pore pressure. The stability factor calculated using the Bishop method (LEM) was compared with the results from FEM and DEM to assess the accuracy and limitations of each technique. The study revealed that the stability factor (FS) decreases as the slope angle and seismic acceleration increase. When the acceleration reaches 0.3 g, the stability factor falls below the critical value (FS < 1.0), indicating an increased likelihood of slope failure. Although FEM and DEM methods provide more accurate modeling of deformations and failure mechanisms, LEM, which is based on static equilibrium, may overestimate slope stability. This research highlights the effectiveness of numerical modeling in predicting slope stability under seismic loading. Future investigations are recommended to develop hybrid models that combine LEM, FEM, and DEM, apply machine learning methods for predictive stability analysis, and consider long-term factors such as soil erosion and cyclic seismic loads. The findings can be utilized to improve slope design safety and enhance the resilience of infrastructure in seismically active regions.

In hot and arid climates, rapid moisture loss from freshly placed cement concrete pavements disrupts hydration, leading to reduced strength and durability. This study evaluates novel film-forming compositions based on secondary industrial resources (GACH-3 and spent mineral oils). The research establishes the physicochemical mechanisms of pore colmatation achieved through the synergistic interaction of paraffin-based components and technical oleic acid. Experimental results demonstrate that the optimized composition increases compressive strength by up to 35.1 % and reduces moisture evaporation twofold. Long-term observations (12 months) confirmed significantly reduced porosity and microcrack sizes.

This paper presents the development of a wearable system for human motion monitoring based on an inertial measurement unit (IMU). The proposed device enables real-time acquisition of angular velocity, linear acceleration, and orientation parameters of a body segment. An experimental prototype was implemented using an IMU sensor, Arduino Nano, and a data recording module. Laboratory tests focused on dorsiflexion and plantarflexion movements of the ankle joint. The results demonstrate that the system can accurately capture motion parameters and reflect changes in the Pitch angle corresponding to these movements. The proposed approach can be applied in motion analysis and rehabilitation monitoring. Future work includes integration of EMG and force sensors to extend the system functionality.

In order to alleviate the fatigue damage caused by resonance in the combined multi-layer linear vibrating screen, this study conducted modal and harmonic response analysis using Workbench finite element analysis software. The results showed that there was a resonance risk at the 9th natural frequency. By optimizing the thickness of the side plates (increasing it by 4 millimeters), the frequency was successfully removed from the working range. This paramete optimization significantly improved the dynamic performance and reliability of the structure, providing a solid theoretical basis for design.

In this paper, the vortex flow structures formed behind a square cylinder and their spectral properties are numerically investigated based on the 2D URANS k-ω SST model (Re ≈ 4.7×104). The study compared locally densified and adaptive anisotropic (Hessian-based) meshes. The calculations were performed using the PISO algorithm and second-order accuracy (CFL ≤ 1, Δt≈T/200). The results were compared with experimental data on Strouhal number, drag and lift coefficients, and velocity profiles. The adaptive mesh more accurately represents the wake zone and sharp gradients of the flow, allows for reliable estimation of spectral parameters (frequency and amplitude), and reduces numerical diffusion. Some limitations of the 2D URANS model are also indicated.

This paper presents a conceptual design of a robotic exoskeleton for active correction of spinal deformities. Unlike existing support-based systems, the proposed approach is correction-oriented and uses a multi-segment structure with linear electric actuators. A comparative analysis of actuation systems was conducted, and the FOCOTECK LA14C actuator was selected for wearable application. The exoskeleton is designed as a vest-based system with actuators mounted via articulated joints to improve kinematic compatibility. A CAD model was developed to evaluate the feasibility of the design. Future work will include simulation and experimental validation using a 3D-printed prototype

The development of high-speed and heavy-haul railway traffic leads to an increase in dynamic stresses in rails, resulting in the formation of hidden fatigue cracks. Such defects are among the most dangerous because they cannot be detected by visual inspection and failure occurs suddenly without warning. In recent years, the primary direction of railway safety improvement has shifted from mere defect detection to quantitative assessment of defect severity and prediction of the rail residual life. This paper analyzes modern rail non-destructive testing (NDT) instruments, including ultrasonic, eddy current, magnetic, acoustic emission, and intelligent diagnostic systems. Their operating principles, sensitivity to various defect types, inspection depth, and fields of application are considered. Particular attention is given to phased array ultrasonic testing and high-speed inspection vehicles providing continuous in-motion monitoring of track condition. A methodology for quantitative assessment of hidden crack severity based on the fracture energy criterion and the stress intensity factor is proposed. A calculation model is developed to determine the probability of rail failure considering defect parameters, stress state, and traffic conditions. A calculation example for a standard R65 rail is presented. The results show that integrating multi-channel defectoscopy with mathematical residual life prediction reduces the probability of rail fracture by more than three times. The obtained relationships can be applied for transitioning from periodic inspections to a risk-based track maintenance system.