Search Results (1,311)

Few-shot fault diagnosis of intelligent high-speed train components remains challenging because fault samples are scarce and highly imbalanced. To address this issue, this paper proposes MR-DDIM, a class-conditional diffusion-based data augmentation framework for generating high-fidelity fault vibration signals from limited labeled data. A WT-UNet denoising backbone is developed by combining one-dimensional wavelet convolution with Feature-Wise Linear Modulation (FiLM) to capture multiscale time–frequency structures and enable class-controllable generation. To improve training stability and spectral fidelity, log-σ regularization and a multi-resolution STFT consistency loss are introduced into the optimization process. In addition, this paper proposed the multi-resolution spectral correlation coefficient (MR-SCC) and class-intrinsic maximum mean discrepancy (cMMD) to evaluate generation quality from spectral and distributional perspectives. Experiments on the BJTU-RAO datasets show that the proposed method can generate fault samples with high spectral consistency and reasonable intra-class diversity, thereby improving the robustness of downstream few-shot fault diagnosis. The results indicate that MR-DDIM provides an effective data augmentation solution for intelligent fault diagnosis in high-speed railway systems. Full article

Regular inspection of defects like sprayed concrete cracking and water seepage is crucial for the long-term safety of reservoir slopes in hydraulic engineering. Traditional manual inspections suffer from low efficiency and high cost. This paper presents a weighted Traveling Salesman Problem (TSP) model established by a Genetic Algorithm (GA) to optimize Unmanned Aerial Vehicle (UAV) inspection paths for these slopes. The model integrates UAV acceleration and deceleration physics. It weights the flight distance, converting it into flight time, and uses 3D-coordinate data to form the objective function. We calibrated key parameters, including acceleration and speed thresholds, by fitting displacement-time quadratic functions to field data from a DJI Matrice 350 RTK UAV. Tests on multiple slope models show the weighted GA optimizes the planned path by 46.2%, improves average inspection efficiency by 7.90% over an algorithm simulating human decision-making, and by 7.66% over a standard (non-weighted) GA. This work provides a reference for intelligent path planning on reservoir slopes and is applicable to similar scenarios like highway and railway slopes. Full article

Optimizing high-speed railway (HSR) timetables requires coordinated decisions on train stopping patterns and local feasibility constraints, such as headway separation and service ordering, particularly at stations where consecutive trains share infrastructure. As network size and service density increase, station-level interactions can induce strategic behavior among trains competing for overlapping passenger markets. This study introduces a delayed-feedback evolutionary game framework to model interactions between two trains under incomplete information. Here, “delayed feedback” represents the lag in information transmission and periodic strategy updates, rather than operational train delays. We first formulate replicator dynamics for the non-delayed scenario, deriving equilibrium points and local stability conditions. The model is then extended to include delayed feedback in payoff evaluation, and the impact of delay parameters on stability and convergence is analyzed. To account for operational heterogeneity, two station layouts are considered: (i) two tracks per direction, representing small stations with limited overtaking and stop–pass combinations, and (ii) four tracks per direction, representing large stations that allow simultaneous stopping and richer operational patterns. Numerical simulations examine convergence, oscillatory behavior, and parameter sensitivity. Results indicate that delayed feedback significantly influences system dynamics: small delays maintain convergence to evolutionarily stable strategies, whereas larger delays induce persistent oscillations and complex transient trajectories. Station layout further affects stability regions and long-term strategy profiles. This study is based on analytical derivation and numerical simulation rather than sample-based statistical inference; therefore, non-parametric hypothesis testing is not applicable in the present framework. This framework provides a game-theoretic and stability-oriented tool for station-level timetable analysis, offering methodological guidance for timetable design under delayed decision feedback in HSR operations. Full article

Traditional rolling stock dynamics studies often rely on simplified 2D models, limiting stability predictions for innovative designs at high speeds. This work proposes a refined spatial multi-mass mathematical model that accounts for nonlinear interrelationships and the superposition of deterministic and random disturbances. This approach enables a detailed reproduction of components with variable stiffness and diagonal connections, identifying critical dependencies inaccessible to standard analytical methods. The model describes spatial vibrations using linear differential equations, considering vertical and horizontal perturbations to simulate real-world operational conditions. To ensure accuracy, the simulation results were validated against field test data, showing high correspondence in force levels and displacements. The study optimizes spring suspension parameters for speeds of 40–140 km/h. Key findings include: Relative friction coefficients (${\phi }_0$) should be adjusted: reduced to 6% for new bogie designs, but increased to 12% for model 18-9996 equipped with diagonal braces. Dynamic stability improves significantly with increased horizontal coupling stiffness. This is achieved through the integration of diagonal braces with side frames and the use of elastic-roller side bearers. This methodology provides a robust framework for evaluating the stability and performance of innovative railway vehicle designs. Full article

This paper investigates an adaptive pilot-assisted channel estimation framework for orthogonal frequency-division multiplexing (OFDM)-based high-speed railway (HSR) communications over non-stationary wideband channels. Within this framework, a channel-aware adaptive pilot insertion (CA-API) mechanism is combined with an linear minimum mean square error (LMMSE) shrinkage technique to adjust pilot density based on temporal channel variations. Using the refined pilot-domain observations, three time-domain channel estimators namely piecewise cubic Hermite interpolation (PCHIP), autoregressive (AR), and Gaussian process regression (GPR), are comparatively evaluated under measurement-based HSR channel models. Simulation results across Remote Area (RA), Closer Area (CEA), and Close Area (CA) conditions demonstrate that the benefit of adaptive pilot scheduling is strongly scenario-dependent. In RA and CEA, the CA-API scheme reduces overhead while maintaining channel reconstruction accuracy close to that of the fixed-pilot baseline, with average overhead reductions of 38% and 30%, respectively. Under the more dispersive CA condition, the adaptive mechanism tends to increase pilot density to preserve reliable channel tracking. Among the evaluated algorithms, GPR delivers the highest estimation accuracy, AR provides a balanced trade-off between accuracy and implementation complexity, and PCHIP is less accurate but remains attractive because of its low complexity. This study provides practical insights into the joint design of adaptive pilot scheduling and channel estimation for HSR wireless communication systems. Full article

In the small-deformation scenario of high-speed railway (HSR) rigid pile–raft composite foundations, the coupled effects of non-equidistant and non-stationary (NENS) characteristics in observational data significantly affect the system response of prediction models. Existing research has predominantly focused on single evaluation metrics or large-deformation scenarios, lacking a comprehensive evaluation system for the multidimensional performance of these models under small-deformation conditions. NENS time-series data were generated via Monte Carlo simulation. The coupled effect was quantified through a process involving “theoretical curve extraction–non-equidistant sampling–random disturbance injection”. Parameters such as pile length, displacement ratio, and pile–soil modulus ratio were normalized using the composite modulus (CMA) method to uniformly characterize the influence of foundation stiffness on time-varying settlement characteristics. A robust entropy-weighted method was then used to construct a comprehensive evaluation index (CEI), which integrates goodness-of-fit (36%), prediction accuracy (26%), and stability (38%) to systematically assess four empirical models: the hyperbolic method, exponential curve method, Asaoka method, and Hoshino method. The results indicate that when CMA ≤ 100 MPa, settlement curves exhibit nonlinearity, and the Hoshino and hyperbolic methods perform optimally. Between 100 and 1000 MPa, pile–soil interaction intensifies, highlighting the Hoshino method’s superior stability. When CMA ≥ 1000 MPa, pile–soil interaction becomes load-dominated, with the Hoshino method remaining optimal while the hyperbolic and exponential curve methods exhibit significantly increased errors. The proposed NENS time-series simulation–multi-criteria coupling evaluation framework resolves model selection challenges in small-deformation scenarios and provides robust decision support for HSR settlement prediction. Full article

This article highlights the intrinsic limitations of existing standards, such as EN 50160 and its associated measurement techniques, when applied to the assessment of power quality in high-speed railway traction power supply networks. These networks, characterized by intermittent and non-linear loads, generate disturbances (harmonics, voltage unbalance) that are not always detected or correctly quantified by standardized aggregation methods, leading to an underestimation of the actual impacts and calling into question the credibility of compliance assessments. The study proposes a new evaluation methodology based on synchronizing measurements with train traffic and grouping data by events rather than by fixed aggregation periods. This approach enables a more accurate characterization of negative-sequence voltage unbalance, providing a reliable estimation of both the magnitude and duration of disturbances. Experimental observations from multiple journeys and aggregation scenarios provide quantitative evidence supporting the relevance of the proposed improvements, which will contribute to updating and implementing standards better adapted to the specific characteristics of intermittent networks such as railway traffic, thereby ensuring a reliable, credible, and reproducible power quality assessment. Full article

Ultra-high-speed railway communication systems face several technical challenges due to extremely high mobility, including Doppler-induced channel variations, frequent handovers, and increasing network traffic. These challenges not only degrade communication reliability but also negatively affect the efficiency of network resource utilization. In this paper, we review the key technical challenges in ultra-high-speed railway communication environments and investigate artificial intelligence (AI)-based intelligent network control techniques to address these issues. In particular, we examine mobility management approaches focusing on AI-based predictive handover schemes and intelligent network control architectures based on the Open Radio Access Network (O-RAN). In addition, network resource management strategies are discussed through mobile edge computing (MEC)-enabled traffic offloading and task migration techniques. Through this analysis, we discuss the potential applicability of intelligent network control technologies for improving communication reliability and enhancing network resource utilization efficiency in ultra-high-speed railway communication environments. Full article

With the rapid development of railway and intelligent transportation systems, the construction of security systems along high-speed railways has attracted more and more attention. In this paper, we propose a fiber distributed acoustic sensing (DAS) intrusion detection system to detect and identify the intrusion events that threaten the operational safety of high-speed railways. Firstly, we use the DAS system to collect the optical fiber signals around the high-speed railway. Then we design a window to slide the optical fiber signals along the time axis to form the intensity images with the spatio-temporal signal features. After that, we propose a novel framework that integrates the feature pyramid network (FPN) and the Faster R-CNN to extract the features from the fiber signal intensity images to improve the detection rate and recognition rate of the system for high-speed railway intrusion events. Experimental results indicate that the system can identify five kinds of intrusion events. The average detection accuracy can reach 95.51%, and the F1 score of each intrusion event is above 93% on the real dataset. In addition, the system can identify the background noise interference generated by passing trains, and the detection accuracy is 95%, which can significantly reduce the false alarm rate. Full article

The impact of short-distance lateral shield tunneling threatens the safety of operational high-speed railways (HSRs). To address the engineering challenge of “how to select isolation pile density under fixed cost constraints,” this study focuses on the Xi’an Metro shield tunnel section passing laterally adjacent to the Daxi and Zhengxi Passenger Dedicated Lines. Under the constraint of identical total economic costs, two isolation pile schemes—low-density and high-density—were established to investigate the control patterns of different densities on HSR bridge piles and surrounding ground surface deformation. A three-dimensional (3D) numerical model was developed for the lateral shield tunneling process. Combined with field-measured data, numerical simulations were conducted for corresponding construction stages to analyze the disturbance effects of shield tunneling on HSR piers and the surrounding ground, as well as the deformation restraint performance of isolation piles. The results indicate that the high-density isolation pile scheme (pile spacing: 2.0 m; pile length: 22 m) provides superior control compared to the low-density scheme (pile spacing: 4 m; pile length: 28 m). Following single- and double-track excavation, the vertical displacement of HSR piers was reduced by 0.6 mm and 1.1 mm, respectively—a reduction of 40–74%. Furthermore, the pier displacement along the depth direction shifted from non-uniform to relatively uniform. The difference in surface settlement between the two schemes was only 0.2 mm, suggesting that isolation pile density has a marginal impact on ground deformation. The horizontal displacement of high-density isolation piles stabilized at 1.7–1.9 mm, with vertical heave ranging from 1.2 to 1.4 mm. The lateral displacement profile exhibited a regular “double-C outward expansion” shape, which is better suited to the characteristics of water-rich sand layers. Initial excavation causes significant disturbance to the original strata, necessitating enhanced stress field protection measures. The high-density scheme is recommended for engineering applications, as it achieves optimal control of bridge pile deformation under cost constraints and meets regulatory specifications. Full article

Extreme meteorological phenomena—characterized by gale-force winds, torrential rainfall, and ice-snow accumulation—pose significant threats to the operational safety of high-speed railways, with wind-induced hazards being especially critical. Such events can trigger catastrophic incidents, including train derailments and service disruptions, as evidenced by numerous documented cases worldwide. To bolster the wind resilience of high-speed railway systems, high-precision wind speed prediction has become a cornerstone for ensuring operational safety. This research presents a systematic review of international advancements in railway wind early warning systems, critically evaluating the technical attributes and performance constraints of four primary paradigms: physical numerical models, statistical methods, machine learning algorithms, and hybrid frameworks. Moving beyond a simple taxonomy, this paper delineates the strengths, limitations, and domain-specific applicability of each approach within the high-speed railways context. Furthermore, it assesses the transformative potential of emerging large-scale Artificial Intelligence (AI) meteorological models for wind speed forecasting. A quantitative comparison is provided to facilitate rigorous methodological assessment. The findings reveal four critical technical bottlenecks: (1) low computational efficiency of numerical models; (2) insufficient spatiotemporal resolution of monitoring data; (3) poor generalization of predictive models; and (4) the “black-box” nature and weak interpretability of AI models. To address these, this paper posits that future research should prioritize key technologies including multi-source heterogeneous data fusion, algorithmic optimization, design of intelligent algorithms, probabilistic risk forecasting, and the synergistic integration of AI with numerical weather prediction (NWP). Such advancements will catalyze the development of more robust HSR wind warning systems, ensuring sustained safety and operational efficiency under volatile meteorological conditions. Full article

The electrification of regional railway lines using battery-electric trains requires accurate simulation tools to support energy management and thermal control design. This paper presents an integrated dynamic simulation model of the traction system of a Hitachi Caravaggio ETR 521 regional train operating in battery-electric mode, developed in MATLAB/Simulink 2024b. The model incorporates all key drivetrain components, including a train reference generator, speed controller, motor controller, three-phase inverter, induction motor, a Kokam Co., Ltd. lithium-ion battery pack, and a detailed battery thermal management system. The proposed framework enables simultaneous evaluation of traction performance, battery state of charge (SOC) evolution, and thermal behavior under realistic conditions. To validate the model, simulations of the Treviso–Vicenza route were conducted under two scenarios: traction-only operation and operation with a 160 kW auxiliary load. Simulation results demonstrate that auxiliary loads significantly affect energy consumption and battery thermal behavior, with energy consumption increased by 50%. The results highlight the importance of integrating thermal effects into energy management and sizing decisions for battery-electric regional trains. The developed model provides a practical tool for optimizing battery sizing, thermal management strategies, and overall energy performance, supporting the planning and design of sustainable electric railway solutions. The modular MATLAB/Simulink architecture is designed to be route-agnostic; extension to other regional lines with different gradients, speed profiles, or extreme climate conditions (e.g., alpine routes or high-temperature regions) requires only updated route data and adjusted ambient boundary conditions, demonstrating the model’s broad applicability beyond the Treviso–Vicenza case study. Full article

Ground-source heat pump (GSHP) systems are widely used because of their energy-saving and environmentally friendly characteristics. However, the long-term operation of a standalone GSHP system leads to heat accumulation in the soil for cooling load-dominated buildings, which results in a decline in system performance. To address this issue, in this study, a high-speed railway station in Jinan was considered as the research object, and a hybrid system scheme in which a GSHP is coupled with an air-source heat pump (ASHP) was developed. The system uses the outdoor dry-bulb temperature as the control parameter and establishes a multi-unit operation control strategy. A dynamic simulation model of the hybrid system was constructed using TRNSYS software, and then the energy consumption, soil thermal balance, economics and environmental benefits of the system under various schemes and operating conditions were simulated and analyzed. Through a comparative analysis of the operating strategies, the optimal strategy that achieved the best performance was determined. Under the optimal strategy, the soil thermal imbalance rate after 10 years of operation was only 1%, the total energy consumption was significantly lower than that of a standalone ASHP system, and the initial investment was clearly lower than that of a standalone GSHP system. The results demonstrate that the hybrid system ensures soil thermal balance and high-efficiency operation while providing significant energy savings (a 28% primary energy savings rate compared to a standalone ASHP) and environmental benefits (reducing annual CO₂, SO₂, NOx, and dust emissions by 56.5 t, 384.2 kg, 361.6 kg, and 339 kg, respectively). Therefore, the emission of atmospheric pollutants such as CO₂, SO₂, NOx, and dust can be effectively reduced, thus providing an important reference for the development of building energy-saving technologies under the “dual carbon” goals. Full article

Track quality is key to ensuring the safety and comfort of passengers and freight in railway systems. However, continuous monitoring is rarely implemented due to its high cost and technical complexity. This paper introduces a methodological framework based on machine learning and optimization algorithms for developing onboard track quality monitoring systems using inertial measurements. The workflow addresses crucial, often overlooked aspects such as sensor location, integrating them with downstream processes. The methodology was validated through its application to longitudinal level quality estimation. Synthetic acceleration signals were generated using multibody simulations under parameter configurations defined through a Design of Experiments framework. A multi-objective optimization approach was applied to determine the optimal combination of sensors, balancing estimation accuracy and efficiency. Among the evaluated models, XGBoost achieved a root mean square error of 0.175 mm on the test set, requiring only two acceleration signals and vehicle speed. The use of features derived from wavelet spectra instead of traditional statistical descriptors reduced the estimation error by approximately 20%. These results demonstrate the feasibility of constructing low-cost, data-driven monitoring systems for track quality assessment and highlight the benefits of a structured methodology integrating data generation, sensor analysis, and learning algorithms. Full article

The eddy current braking system (ECBS) is a crucial non-contact technology for high-speed railway. Conventional DC-excited systems face significant challenges such as excessive rail heating and high-capacity power supply requirements. This paper proposes a novel ECBS with AC excitation and auxiliary capacitor to achieve integrated energy recovery and power supply optimization. To evaluate its performance, a rigorous analytical framework is developed. First, a 2D subdomain model is established by incorporating the longitudinal end effect to solve the magnetic field distribution. Subsequently, an equivalent circuit is derived from the subdomain results to investigate steady-state braking characteristics and power flow. Analysis results demonstrate that the proposed system not only generates controllable braking force but also converts a portion of kinetic energy into storable electrical energy, effectively mitigating secondary rail heating. Most significantly, the implementation of an optimal auxiliary capacitor (134 μF) is found to reduce the required inverter capacity compared to inverter-only conditions. These findings provide a theoretical foundation and a practical design tool for developing high-performance, energy-efficient braking systems in high-speed transportation. Full article