Traction power systems for electrified railways:evolution,state of the art,and future trends

Traction power systems(TPSs)play a vital role in the operation of electrified railways.The transformation of conventional railway TPSs to novel structures is not only a trend to promote the development of electrified railways toward high-efficiency and resilience but also an inevitable requirement to achieve carbon neutrality target.On the basis of sorting out the power supply structures of conventional AC and DC modes,this paper first reviews the characteristics of the existing TPSs,such as weak power supply flexibility and low-energy efficiency.Furthermore,the power supply structures of various TPSs for future electrified railways are described in detail,which satisfy longer distance,low-carbon,high-efficiency,high-reliability and high-quality power supply requirements.Meanwhile,the application prospects of different traction modes are discussed from both technical and economic aspects.Eventually,this paper introduces the research progress of mixed-system electrified railways and traction power supply technologies without catenary system,speculates on the future development trends and challenges of TPSs and predicts that TPSs will be based on the continuous power supply mode,employing power electronic equipment and intelligent information technology to construct a railway comprehensive energy system with renewable energy.

A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.

A discussion about the limitations of the Eurocode’s high-speed load model for railway bridges

High-speed railway bridges are subjected to normative limitations concerning maximum permissible deck accelerations.For the design of these structures,the European norm EN 1991-2 introduces the high-speed load model(HSLM)—a set of point loads intended to include the effects of existing high-speed trains.Yet,the evolution of current trains and the recent development of new load models motivate a discussion regarding the limits of validity of the HSLM.For this study,a large number of randomly generated load models of articulated,conventional,and regular trains are tested and compared with the envelope of HSLM effects.For each type of train,two sets of 100,000 load models are considered:one abiding by the limits of the EN 1991-2 and another considering wider limits.This comparison is achieved using both a bridge-independent metric(train signatures)and dynamic analyses on a case study bridge(the Canelas bridge of the Portuguese Railway Network).For the latter,a methodology to decrease the computational cost of moving loads analysis is introduced.Results show that some theoretical load models constructed within the stipulated limits of the norm can lead to effects not covered by the HSLM.This is especially noted in conventional trains,where there is a relation with larger distances between centres of adjacent vehicle bogies.

Unraveling engineering disturbance effects on deformation in red-bed mudstone railway cuttings:incorporating crack-facilitated moisture diffusion

Red-bed mudstone, prevalent in southwest China, poses a formidable challenge due to its hydrophilic clay minerals, resulting in expansion, deformation, and cracking upon exposure to moisture. This study addresses uplift deformation disasters in high-speed railways by employing a moisture diffusion-deformation-fracture coupling model based on the finite-discrete element method(FDEM). The model integrates the influence of cracks on moisture diffusion. The investigation into various excavation depths reveals a direct correlation between depth and the formation of tensile cracks at the bottom of the railway cutting. These cracks expedite moisture migration, significantly impacting the temporal and spatial evolution of the moisture field. Additionally, crack expansion dominates hygroscopic deformation, with the lateral coordinate of the crack zone determining peak vertical displacement. Furthermore, key factors influencing deformation in railway cuttings, including the swelling factor and initial moisture content at the bottom of the cutting, are explored. The number of tensile and shear cracks increases with greater excavation depth, particularly concerning shear cracks. Higher swelling factors and initial moisture contents result in an increased total number of cracks, predominantly shear cracks. Numerical calculations provide valuable insights, offering a scientific foundation and directional guidance for the precise prevention, control, prediction, and comprehensive treatment of mudstone-related issues in high-speed railways.

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Formation and ecological response of sand patches in the protection system of Shapotou section of the Baotou-Lanzhou railway,China

The development of bare patches typically signifies a process of ecosystem degradation.Within the protection system of Shapotou section of the Baotou-Lanzhou railway,the extensive emergence of bare sand patches poses a threat to both stability and sustainability.However,there is limited knowledge regarding the morphology,dynamic changes,and ecological responses associated with these sand patches.Therefore,we analyzed the formation and development process of sand patches within the protection system and its effects on herbaceous vegetation growth and soil nutrients through field observation,survey,and indoor analysis methods.The results showed that sand patch development can be divided into three stages,i.e.,formation,expansion,and stabilization,which correspond to the initial,actively developing,and semi-fixed sand patches,respectively.The average dimensions of all sand patch erosional areas were found to be 7.72 m in length,3.91 m in width,and 0.32 m in depth.The actively developing sand patches were the largest,and the initial sand patches were the smallest.Throughout the stage of formation and expansion,the herbaceous community composition changed,and the plant density decreased by more than 50.95%.Moreover,the coverage and height of herbaceous plants decreased in the erosional area and slightly increased in the depositional lobe;and the fine particles and nutrients of soils in the erosional area and depositional lobe showed a decreasing trend.In the stabilization phases of sand patches,the area from the inlet to the bottom of sand patches becomes initially covered with crusts.Vegetation and 0-2 cm surface soil condition improved in the erosional area,but this improvement was not yet evident in the depositional lobe.Factors such as disturbance,climate change,and surface resistance to erosion exert notable influences on the formation and dynamics of sand patches.The results can provide evidence for the future treatment of sand patches and the management of the protection system of Shapotou section of the Baotou-Lanzhou railway.

Influence of pier height on the safety of trains running on high-speed railway bridges during earthquakes

Sudden earthquakes pose a threat to the running safety of trains on high-speed railway bridges,and the stiffness of piers is one of the factors affecting the dynamic response of train-track-bridge system.In this paper,a experiment of a train running on a high-speed railway bridge is performed based on a dynamic experiment system,and the corresponding numerical model is established.The reliability of the numerical model is verified by experiments.Then,the experiment and numerical data are analyzed to reveal the pier height effects on the running safety of trains on bridges.The results show that when the pier height changes,the frequency of the bridge below the 30 m pier height changes greater;the increase of pier height causes the transverse fundamental frequency of the bridge close to that of the train,and the shaking angle and lateral displacement of the train are the largest for bridge with 50 m pier,which increases the risk of derailment;with the pier height increases from 8 m to 50 m,the derailment coefficient obtained by numerical simulations increases by 75% on average,and the spectral intensity obtained by experiments increases by 120% on average,two indicators exhibit logarithmic variation.

Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

Research on the path of effective management of participation in joint-venture railway companies by China railway group

Purpose-Following the regional restructuring,the number of joint-venture railway companies in which the Group participates has significantly increased.This paper aims to explore the challenges faced by China Railway Group in managing participation in joint-venture railway companies.The study seeks to propose specific approaches to ensure the effective management of these companies,thereby maximizing the benefits of the regional restructuring and supporting the development of a strong transportation country and a modern infrastructure system.Design/methodology/approach-Based on the change in the shareholding relationship between China Railway Group and the joint-venture railway companies,and considering the current situation of the regional restructuring of these companies,as well as the insights from existing literature and typical case studies,this paper proposes some specific paths for effective management of joint-stock railway companies which China Railway Group participated in.Findings-The problems in participation management are the unclear dual leadership role of the party committee,the lack of discourse power,the lack of synergy between shareholders,the increasing risk of sustainable operation of the loss-making companies and the role of dispatched personnel is not fully played.Based on the theories,combined with the existing research and practical cases,the paper proposed specific approaches,such as perfecting top-level system design,maintaining the discourse power,carrying out differentiated management,arranging personnel rationally,arranging shareholders synergy,and innovating methods to provide references for China Railway Group’s subsequent management of joint venture railway companies.Originality/value-This paper contributes to the existing literature by providing a comprehensive analysis of the challenges faced by China Railway Group in managing participation in joint-venture railway companies following the regional restructuring.The study offers novel insights and practical recommendations for addressing these challenges.The findings can serve as valuable references for China Railway Group’s subsequent management of joint-venture railway companies which participated in,as well as for other stateowned enterprises facing similar challenges in managing their joint ventures.

Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine

Railway switch machine is essential for maintaining the safety and punctuality of train operations.A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed herein.Unlike existing methods,this approach takes into account the spatial information of the time series monitoring data,aligning with the domain expertise of on-site manual monitoring.Besides,a multi-sensor fusion tensor machine is designed to improve single signal data’s limitations in insufficient information.First,one-dimensional signal data is preprocessed and transformed into two-dimensional images.Afterward,the fusion feature tensor is created by utilizing the images of the three-phase current and employing the CANDE-COMP/PARAFAC(CP)decomposition method.Then,the tensor learning-based model is built using the extracted fusion feature tensor.The developed fault diagnosis scheme is valid with the field three-phase current dataset.The experiment indicates an enhanced performance of the developed fault diagnosis scheme over the current approach,particularly in terms of recall,precision,and F1-score.

Field survey and analysis on near-fault severely damaged high-speed railway bridge in 2022 M6.9 Menyuan earthquake

The 2022 M6.9 Menyuan earthquake caused severe damage to a high-speed railway bridge,which was designed for high-speed trains running at speeds of above 250 km/h and is located right next to the fault.Bridges of this type have been widely used for rapidly constructing the high-speed railway network,but few bridges have been tested by near-fault devastating earthquakes.The potential severe impact of the earthquake on the high-speed railway is not only the safety of the infrastructure,trains and passengers,but also economic loss due to interrupted railway use.Therefore,a field survey was carried out immediately after the earthquake to collect time-sensitive data.The damage to the bridge was carefully investigated,and quantitative analyses were conducted to better understand the mechanism of the bridge failure.It was found that seismic action perpendicular to the bridge’s longitudinal direction caused severe damage to the girders and rails,while none of the piers showed obvious deformation or cracking.The maximum values of transverse displacement,out-of-plane rotation and twisting angle of girders reached 212.6 cm,3.1 degrees and 19.9 degrees,respectively,causing severe damage to the bearing supports and anti-seismic retaining blocks.These observations provide a basis for improving the seismic design of high-speed railway bridges located in near-fault areas.

YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments

Railway turnouts often develop defects such as chipping,cracks,and wear during use.If not detected and addressed promptly,these defects can pose significant risks to train operation safety and passenger security.Despite advances in defect detection technologies,research specifically targeting railway turnout defects remains limited.To address this gap,we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments.To enhance detection accuracy,we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU(YOLO-VSI).The model employs a state-space model(SSM)to enhance the C2f module in the YOLOv8 backbone,proposed the C2f-VSS module to better capture long-range dependencies and contextual features,thus improving feature extraction in complex environments.In the network’s neck layer,we integrate SPDConv and Omni-Kernel Network(OKM)modules to improve the original PAFPN(Path Aggregation Feature Pyramid Network)structure,and proposed the Small Object Upgrade Pyramid(SOUP)structure to enhance small object detection capabilities.Additionally,the Inner-CIoU loss function with a scale factor is applied to further enhance the model’s detection capabilities.Compared to the baseline model,YOLO-VSI demonstrates a 3.5%improvement in average precision on our railway turnout dataset,showcasing increased accuracy and robustness.Experiments on the public NEU-DET dataset reveal a 2.3%increase in average precision over the baseline,indicating that YOLO-VSI has good generalization capabilities.

Deformation monitoring of long-span railway bridges based on SBAS-InSAR technology

The deformation monitoring of long-span railway bridges is significant to ensure the safety of human life and property.The interferometric synthetic aperture radar(In SAR)technology has the advantage of high accuracy in bridge deformation monitoring.This study monitored the deformation of the Ganjiang Super Bridge based on the small baseline subsets(SBAS)In SAR technology and Sentinel-1A data.We analyzed the deformation results combined with bridge structure,temperature,and riverbed sediment scouring.The results are as follows:(1)The Ganjiang Super Bridge area is stable overall,with deformation rates ranging from-15.6 mm/yr to 10.7 mm/yr(2)The settlement of the Ganjiang Super Bridge deck gradually increases from the bridge tower toward the main span,which conforms to the typical deformation pattern of a cable-stayed bridge.(3)The sediment scouring from the riverbed cause the serious settlement on the bridge’s east side compared with that on the west side.(4)The bridge deformation negatively correlates with temperature,with a faster settlement at a higher temperature and a slow rebound trend at a lower temperature.The study findings can provide scientific data support for the health monitoring of long-span railway bridges.

A framework for dynamic modelling of railway track switches considering the switch blades,actuators and control systems

The main contribution of this paper is the development and demonstration of a novel methodology that can be followed to develop a simulation twin of a railway track switch system to test the functionality in a digital environment.This is important because,globally,railway track switches are used to allow trains to change routes;they are a key part of all railway networks.However,because track switches are single points of failure and safety-critical,their inability to operate correctly can cause significant delays and concomitant costs.In order to better understand the dynamic behaviour of switches during operation,this paper has developed a full simulation twin of a complete track switch system.The approach fuses finite element for the rail bending and motion,with physics-based models of the electromechanical actuator system and the control system.Hence,it provides researchers and engineers the opportunity to explore and understand the design space around the dynamic operation of new switches and switch machines before they are built.This is useful for looking at the modification or monitoring of existing switches,and it becomes even more important when new switch concepts are being considered and evaluated.The simulation is capable of running in real time or faster meaning designs can be iterated and checked interactively.The paper describes the modelling approach,demonstrates the methodology by developing the system model for a novel“REPOINT”switch system,and evaluates the system level performance against the dynamic performance requirements for the switch.In the context of that case study,it is found that the proposed new actuation system as designed can meet(and exceed)the system performance requirements,and that the fault tolerance built into the actuation ensures continued operation after a single actuator failure.

Durability evaluation for existing railway engineering: a review

Purpose – This study aims to analyze the factors, evaluation techniques of the durability of existing railwayengineering.Design/methodology/approach – China has built a railway network of over 150,000 km. Ensuring thesafety of the existing railway engineering is of great significance for maintaining normal railway operationorder. However, railway engineering is a strip structure that crosses multiple complex environments. Andrailway engineering will withstand high-frequency impact loads from trains. The above factors have led todifferences in the deterioration characteristics and maintenance strategies of railway engineering compared toconventional concrete structures. Therefore, it is very important to analyze the key factors that affect thedurability of railway structures and propose technologies for durability evaluation.Findings – The factors that affect the durability and reliability of railway engineering are mainly divided intothree categories: material factors, environmental factors and load factors. Among them, material factors alsoinclude influencing factors, such as raw materials, mix proportions and so on. Environmental factors varydepending on the service environment of railway engineering, and the durability and deterioration of concretehave different failure mechanisms. Load factors include static load and train dynamic load. The on-site rapiddetection methods for five common diseases in railway engineering are also proposed in this paper. Thesemethods can quickly evaluate the durability of existing railway engineering concrete.Originality/value – The research can provide some new evaluation techniques and methods for thedurability of existing railway engineering.

Artificial-intelligent-powered safety and efficiency improvement for controlling and scheduling in integrated railway systems

The multi-mode integrated railway system,anchored by the high-speed railway,caters to the diverse travel requirements both within and between cities,offering safe,comfortable,punctual,and eco-friendly transportation services.With the expansion of the railway networks,enhancing the efficiency and safety of the comprehensive system has become a crucial issue in the advanced development of railway transportation.In light of the prevailing application of artificial intelligence technologies within railway systems,this study leverages large model technology characterized by robust learning capabilities,efficient associative abilities,and linkage analysis to propose an Artificial-intelligent(AI)-powered railway control and dispatching system.This system is elaborately designed with four core functions,including global optimum unattended dispatching,synergetic transportation in multiple modes,high-speed automatic control,and precise maintenance decision and execution.The deployment pathway and essential tasks of the system are further delineated,alongside the challenges and obstacles encountered.The AI-powered system promises a significant enhancement in the operational efficiency and safety of the composite railway system,ensuring a more effective alignment between transportation services and passenger demands.

Stability analysis of loose accumulation slopes under rainfall:case study of a high‑speed railway in Southwest China

The high and steep slopes along a high-speed railway in the mountainous area of Southwest China are mostly composed of loose accumulations of debris with large internal pores and poor stability,which can easily induce adverse geological disasters under rainfall conditions.To ensure the smooth construction of the high-speed railway and the subsequent safe operation,it is necessary to master the stability evolution process of the loose accumulation slope under rainfall.This article simulates rainfall using the finite element analysis software’s hydromechanical coupling module.The slope stability under various rainfall situations is calculated and analysed based on the strength reduction method.To validate the simulation results,a field monitoring system is established to study the deformation characteristics of the slope under rainfall.The results show that rainfall duration is the key factor affecting slope stability.Given a constant amount of rainfall,the stability of the slope decreases with increasing duration of rainfall.Moreover,when the amount and duration of rainfall are constant,continuous rainfall has a greater impact on slope stability than intermittent rainfall.The setting of the field retaining structures has a significant role in improving slope stability.The field monitoring data show that the slope is in the initial deformation stage and has good stability,which verifies the rationality of the numerical simulation method.The research results can provide some references for understanding the influence of rainfall on the stability of loose accumulation slopes along high-speed railways and establishing a monitoring system.

Remaining Useful Life for Heavy-Duty Railway Cast Steel Knuckles Based on Crack Growth Behavior with Hypothetical Distributions

The current research on the integrity of critical structures of rail vehicles mainly focuses on the design stage,which needs an effective method for assessing the service state.This paper proposes a framework for predicting the remaining useful life(RUL)of in-service structures with and without visible cracks.The hypothetical distribution and delay time models were used to apply the equivalent crack growth life data of heavy-duty railway cast steel knuckles,which revealed the evolution characteristics of the crack length and life scores of the knuckle under different fracture failure modes.The results indicate that the method effectively predicts the RUL of service knuckles in different failure modes based on the cumulative failure probability curves for different locations and surface crack lengths.This study proposes an RUL prediction framework that supports the dynamic overhaul and state maintenance of knuckle fatigue cracks.

Numerical modeling techniques for noise emission of free railway wheels

In this article,we consider the numerical prediction of the noise emission from a wheelset in laboratory conditions.We focus on the fluid-structure interaction leading to sound emission in the fluid domain by analyzing three different methods to account for acoustic sources.These are a discretized baffled piston using the discrete calculation method(DCM),a closed cylindrical volume using the boundary element method(BEM)and radiating elastic disks in a cubic enclosure solved with the finite element method(FEM).We provide the validation of the baffled piston and the BEM using measurements of the noise emission of a railway wheel by considering ground reflections in the numerical models.Selected space-resolved waveforms are compared with experimental results as well as with a fluid-structure interaction finite element model.The computational advantage of a discretized disk mounted on a baffle and BEM compared to FEM is highlighted,and the baffled pistons limitations caused by a lack of edge radiation effects are investigated.

Reflection on methodology of the correlation between electromagnetic interference and safety in high-speed railway

Purpose – In the continuous development of high-speed railways, ensuring the safety of the operation controlsystem is crucial. Electromagnetic interference (EMI) faults in signaling equipment may cause transportationinterruptions, delays and even threaten the safety of train operations. Exploring the impact of disturbances onsignaling equipment and establishing evaluation methods for the correlation between EMI and safety isurgently needed.Design/methodology/approach – This paper elaborates on the necessity and significance of studying theimpact of EMI as an unavoidable and widespread risk factor in the external environment of high-speed railwayoperations and continuous development. The current status of research methods and achievements from theperspectives of standard systems, reliability analysis and safety assessment are examined layer by layer.Additionally, it provides prospects for innovative ideas for exploring the quantitative correlation between EMIand signaling safety.Findings – Despite certain innovative achievements in both domestic and international standard systems andrelated research for ensuring and evaluating railway signaling safety, there’s a lack of quantitative and strategic research on the degradation of safety performance in signaling equipment due to EMI. A quantitativecorrelation between EMI and safety has yet to be established. On this basis, this paper proposes considerationsfor research methods pertaining to the correlation between EMI and safety.Originality/value – This paper overviews a series of methods and outcomes derived from domestic andinternational studies regarding railway signaling safety, encompassing standard systems, reliability analysisand safety assessment. Recognizing the necessity for quantitatively describing and predicting the impact ofEMI on high-speed railway signaling safety, an innovative approach using risk assessment techniques as abridge to establish the correlation between EMI and signaling safety is proposed.