Railway Applications Research Articles

Intelligent railroad inspection and monitoring

Railways are essential to the global transportation infrastructure, providing eco-friendly and economical solutions for the movement of freight and passengers. Inspecting and maintaining extensive rail networks timely poses significant challenges. My group and collaborators have focused on automated railroad inspection technologies, emphasizing the use of deep learning and computer vision to overcome the limitations of traditional manual inspections. Our research introduces groundbreaking real-time inspection methods, leveraging a specialized dataset of railroad components for enhanced instance segmentation models, achieving unprecedented accuracy and inference speeds. The developed computer vision systems efficiently detect track components and their changes over time, and also quantify rail surface defects. Additionally, our work extends to improving railroad crossing safety, utilizing deep learning frameworks for the detection of unusual pedestrian behaviors and object identification, aimed at reducing crossing incidents and improving emergency response times. Our future research directions aim to further refine the cost-effectiveness and autonomy of railroad inspection systems. Through these innovations, we hope to aid in the inspection and maintenance of railroads, offering practical solutions for railroad and other civil engineering applications.

Multi-objective optimization of electro-pneumatic braking process with fuzzy logic control for heavy haul railway applications

ABSTRACT This paper presents a novel approach to multi-objective optimization in heavy haul trains’ electro-pneumatic braking systems using optimizable fuzzy control. Initial findings indicate that a straightforward replacement of pneumatically controlled valves with electro-pneumatic ones, without significant control schedule modifications, fails to substantially improve the dynamics behaviour of rolling-stock. Consequently, a more sophisticated strategy based on optimizable fuzzy logic control for the electro-pneumatic valves was developed. This approach utilizes multi-objective optimization to simultaneously minimize energy dissipation during braking and reduce force peaks in freight car draft gears. The optimal solution achieved demonstrates a 13.41% reduction in kinetic energy dissipation during downhill sections, along with a concurrent decrease in draft gear peak forces by 14.22% in traction and 3.16% in buff regimes compared to standard rolling-stock with pneumatically controlled valves. This optimized fuzzy braking controller outperforms standard procedures, highlighting its potential to enhance heavy haul train efficiency while adhering to safety standards.

Event-driven piezoelectric energy harvesting for railway field applications

Piezoelectric energy harvesters (PEHs) hold promises for revolutionizing railway structural health monitoring (SHM). However, the challenges of designing high-robust PEHs for handling complex and ever-shifting vibrations in practical railway scenarios remain formidable. Unlike conventional methods that align the fundamental frequency of the PEH with the dominant frequency of the vibration source, this study employs an event-driven enhancement method. This study commences by employing a high-fidelity dynamic model of the PEH and an advanced vehicle-track coupled dynamic model to evaluate energy harvesting efficiency across various railway scenarios. The vehicle-induced track vibrations are predicted across various train speeds, track structures, and track irregularities, considering 12 distinct vehicle-track conditions and totaling 3600 simulation datasets, each representing a specific event. We employ the particle swarm optimization (PSO) algorithm to identify optimal PEH designs for different events. To streamline this process, instead of offering one optimal solution for each event, we employ the K-means algorithm to cluster similar events. Subsequently, we choose the centroid design for each cluster as the representative solution for events within that cluster. This approach allows us to use a limited number of representative PEHs to effectively address most events across different clusters. Finally, we thoroughly examine and evaluate the dynamic responses of these representative designs to demonstrate their robust performance. This study explores PEH design optimization from a fresh perspective, bridging the gap between theoretical design and practical implementation in rail transportation systems.

A step-by-step experimental characterization of a low-height acoustic barrier for railway applications

ABSTRACT The development of rail transport is crucial for transitioning towards a more sustainable form of mobility. It is imperative to address and mitigate issues regarding discomfort and noise pollution caused by rail traffic in order to foster a harmonious coexistence between the population and railway transport. In this paper, an experimental prototype of a low height acoustic barrier is characterized. The barrier has a high capacity for mitigating railway noise and has great potential for urban design. The acoustic barrier prototypes were initially developed using numerical models and subsequently subjected to a series of experimental tests. The initial experimental characterization was carried out in a semi-anechoic chamber. In the second phase, the prototypes were tested in a free field. The final phase of the experimental characterization involves the application of the noise barrier prototypes along a section of the Sintra railway line, located near Lisboa, Portugal. The results demonstrate the significant potential for noise mitigation offered by the developed solutions, achieving insertion loss levels of approximately 10 dB across the entire frequency range.

Withdrawn: The Influence of Compression Molding Duration on the Properties of Composite Brake Shoes for Railway Application

Withdrawal: The Influence of Compression Molding Duration On The Properties of Composite Brake Shoes For Railway Application, published in Polymer International, PI.6615.ART. The above article, published online on 29 January 2024 in Wiley Online Library (wileyonlinelibrary.com) as an Accepted Article, has been withdrawn by agreement between the journal Editor in Chief, Timothy E. Long, Society of Chemical Industry, and John Wiley & Sons Ltd. The withdrawal has been agreed at the editorial office's request because of erroneously conduction. © 2024 Society of Industrial Chemistry.

Microstructure and Corrosion of Mg-Based Composites Produced from Custom-Made Powders of AZ31 and Ti6Al4V via Pulse Plasma Sintering.

Magnesium (Mg) and its alloys offer promise for aerospace, railway, and 3D technology applications, yet their inherent limitations, including inadequate strength, pose challenges. Magnesium matrix composites, particularly with metallic reinforcements like titanium (Ti) and its alloys, present a viable solution. Therefore, this study investigates the impact of Ti6Al4V reinforcement on AZ31 magnesium alloy composites produced using pulse plasma sintering (PPS). Results show enhanced microhardness of the materials due to improved densification and microstructural refinement. However, Ti6Al4V addition decreased corrosion resistance, leading to strong microgalvanic corrosion and substrate dissolution. Understanding these effects is crucial for designing Mg-based materials for industries like petrochemicals, where degradation-resistant materials are vital for high-pressure environments. This research provides valuable insights into developing Mg-Ti6Al4V composites with tailored properties for diverse industrial applications, highlighting the importance of considering corrosion behavior in material design. Further investigation is warranted to establish predictive correlations between Ti6Al4V content and corrosion rate for optimizing composite performance.

Determination of Train Vehicle Speed using Fibre Bragg Grating Sensors for Railway Application

The average speed of the train can be determined by dividing the distance by the time taken to reach the targeted destination. Nevertheless, in the process of railroad travel, the pace of the train can either slow down or speed up depending on the circumstances. It is essential to conduct research into the speed of the railroad in particular conditions to ascertain what speed is appropriate for the railroad to run safely under certain conditions. This study is currently being conducted using Fibre Bragg Grating (FBG) sensors to determine the speed of the train at a specific point. Three different specific points are chosen. The speed is calculated by dividing the distance between two wheels on one bogie by the time between the two peaks representing the two wheels on one bogie. The train traveling between each point exhibits three different speed behaviours. It is currently found that the average speed at location 1 is 49.71 km/h, and the train is decelerating. At location 2, the average speed is 66.21 km/h, and the train moves at a constant speed. Lastly, at location 3, the average speed is 40.18 km/h, and the train is accelerating. In addition, the wavelength shifting signal can be utilized to count the train's axles. In this experiment, the train consists of 9 bogies with 18 sets of wheels. Therefore, this experiment demonstrates that FBG sensors can be used to determine the speed of the train at a certain location.

High precision rail surface obstacle detection algorithm based on 3D imaging LiDAR

Railway perimeter intrusion detection is of great significance for ensuring railway safety operations. In this paper, a Light Detection and Ranging (LiDAR) system is designed and developed based on the specific environmental requirements of railway applications. The system is capable of autonomously adjusting the imaging range and angular resolution, enabling high-precision imaging within a 50 m monitoring area. Additionally, based on registration and a series of filtering algorithms, a railway surface obstacle extraction algorithm is developed. The algorithm achieves a 100 % detection rate for obstacles with a minimum height of 15 cm at any position within the monitoring area, and a 70 % detection rate for obstacles with a height of 10 cm. In this algorithm, a coarse registration method based on global planar features combined with the Iterative Closest Point (ICP) registration algorithm achieves sub-centimeter registration accuracy for consecutive point cloud frames. The filtering algorithm based on local planar features enables precise extraction of geometric changes in the railway surface, with simple calculations and fast processing speed, making it highly valuable for extracting object edge features.

Railway Cloud: Management and Orchestration Functionality Designed as Microservices

The cloudification and virtualisation of railway functions have the potential to improve railway operation efficiency, reliability, safety, and security, as well as to enhance passenger experience by offering innovative services. This paper considers issues related to the management and orchestration of railway clouds that host cloudified railway functions. A microservices-based approach to the design of railway cloud management and orchestration functionality is proposed. The basic railway cloud concepts were defined, and functionality related to the basic orchestration of the railway cloud and deployments is analysed in order to derive the requirements of platform resources and workload management. This functionality is further designed in the form of microservices, meaning that they could possibly be used in orchestration applications to enable improvements in scalability, fault isolation, and data security. The design of microservices follows the principles of the Representational State of Transfer (REST) application programming interface (API) as a set of interlinked resources. Resources related to railway cloud orchestration are identified with their associated data, relationships to other resources, and applicable methods. The resources’ methods are used in railway applications to implement the required orchestration functionality and to maintain the state of railway cloud orchestration processes. To verify the synthesised microservices, the common orchestration application logic and microservices’ logic were modelled, and it was proved that the orchestration processes, which run concurrently, expose equivalent behaviour. The proposed approach was validated using a simulation, aiming to evaluate injected latency as a key performance indicator for the reliability and safety of railway operations. Additionally, some safety and security issues related to railway cloud management and orchestration are considered.

Optimal Transport Theory for Railway Track Safety Applications

Abstract Optimal mass transport (OMT) is an optimization problem that determines a method of moving mass or value from one function to another in the most efficiently and optimally. Using the ideas of OMT, this technical note aims to formulate and solve an OMT model for railway track safety. The inputs for the analysis include (a) geometry data, (b) rail defect, (c) subsurface condition, and (d) tonnage. The technical note only presents the steps in achieving a complete OMT analysis. Therefore, the idea of the note is to serve as an illustration of the effectiveness of OMT in railway engineering applications.

Harmonics suppression in high-speed railway via single-phase traction converter with an LCL filter using fuzzy logic control strategy

Introduction. The railway Traction Power Supply System (TPSS) encounters a common challenge related to high-frequency harmonic resonance, especially when employing AC-DC-AC traction drive systems in high-speed trains. This resonance issue arises when the harmonic elements introduced by the traction AC-DC converter on the grid side of trains align with the innate resonance frequency of the TPSS. The novelty the proposed work focuses on the challenges associated with resonance elevation and high-frequency harmonics in high-speed trains, while simultaneously enhancing energy quality. This is achieved by integrating a pulse-width-modulated converter on the grid side with a single-phase configuration and incorporating an LCL filter. Methodology. In order to optimize the system’s efficiency, a robust control system is employed, taking advantage of the capabilities of a fuzzy logic controller (FLC). The choice of the FLC is justified by its straightforward design and reliability, emphasizing the dedication to precise control, as fuzzy logic excels in handling complex, nonlinear systems. Through the use of linguistic variables and heuristic reasoning, the FLC adjusts to dynamic changes in the system, demonstrating its efficacy in enhancing both transient and steady-state responses. Practical value. A grid-side LCL filter-based converter was meticulously designed and rigorously simulated using the MATLAB/Simulink platform. The inclusion of an advanced FLC in the system introduced a novel approach to control strategies, surpassing the traditional PI controller. Through a comprehensive comparative analysis, the simulation results showcased the remarkable efficacy of the proposed solution in an effectively mitigating high-frequency resonance within the TPSS. This outcome underscores the potential of FLC as a sophisticated control mechanism for enhancing the performance systems in railway applications, showcasing its superiority over conventional control methods. The study contributes in shedding light on innovative approaches for optimizing the control and efficiency of grid-side LCL filter-based converters in high-speed train systems.

Thermal model of DC conductors for railway traction networks: A hosting capacity assessment

In this paper, the authors have adapted existing thermal models for the calculation of conductor temperature for use in electrical distribution systems for railway applications in multi-train environments. For this purpose, the authors have used existing standards such as IEEE 738 for overhead lines and have proposed a resolution methodology based on the difference equation model for cables. The authors have opted for a simplified method since the priority in this case is the resolution in systems with multiple stations, lines, and trains. For this same reason, they have chosen to decouple the electrical and thermal calculations so that the electrical variables calculated through load flows constitute the input to the thermal calculation problem. The paper reviews the existing formulation and proposes models for specific use in railway applications in a multi-train environment. A detailed analysis of the results obtained in a real case study is presented as a validation element.

Retrofit of Diesel Engines with H2 for Potential Decarbonization of Non-Electrified Railways: Assessment with Lifecycle Analysis and Advanced Numerical Modeling

The application of hydrogen in heavy-duty vehicles or trains has been suggested as a promising solution to decarbonize the transportation sector. In this study, a one-dimensional engine modeling is employed to evaluate the potential of hydrogen as a fuel for railway applications. A turbocharged diesel engine is simulated as the baseline unit, and the results are validated with experimental data. The same engine is converted to become compatible with hydrogen through some modifications in the turbocharger group and the injection and ignition systems to preserve the performance of the baseline configuration. The findings show that the engine traction power is reduced from 600 to 400 kW, indicating an inferior performance for the hydrogen-fueled engine. The energy consumption of the hydrogen-fueled engine on a real train mission profile is almost two times the diesel version. However, our Life Cycle Assessment analysis with a Well-to-Wheel system boundary shows a 56% reduction in equivalent CO2 emissions for the engine fueled with photovoltaic-based green hydrogen. Substituting diesel with low-carbon hydrogen can decrease the train’s carbon footprint from 4.27 to even less than 2 kg CO2 eq./km, suggesting that moderately modified engines are a promising solution for decarbonizing non-feasibly electrified railway sections.

Integrating AI and DTs: challenges and opportunities in railway maintenance application and beyond

In the last years, there has been a growing interest in the emerging concept of digital twin (DT) as it represents a promising paradigm to continuously monitor cyber–physical systems, as well as to test and validate predictability, safety, and reliability aspects. At the same time, artificial intelligence (AI) is exponentially affirming as an extremely powerful tool when it comes to modeling the behavior of physical assets allowing, de facto, the possibility of making predictions on their potential evolution. However, despite the fact that DTs and AI (and their combination) can act as game-changing technologies in different domains (including the railways), several challenges have to be faced to ensure their effectiveness, especially when dealing with safety-critical systems. This paper provides a narrative review of the scientific literature on DTs for railway maintenance applications, with a special focus on their relationship with AI. The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation.

Laboratory robustness validation of a humidity sensor system for the condition monitoring of grease-lubricated components for railway applications

Abstract. The condition monitoring of the health status of lubricating greases used in axle box bearings can be realized by applying well-established electrical or optical measurement principles. Furthermore, some novel methods have been reported that make use of humidity sensors or of dielectric thermoscopy. One of the most important grease condition parameters is the water content of the lubricating grease, as water can degrade grease to the point that it is no longer able to provide suitable lubrication and can also damage the bearing due to corrosion and cavitation. In this study, a new approach for water detection in lubricated wagon components is presented that is based on commercially available humidity sensors. The core element of this sensor system is a robust humidity sensor mounted in the immediate atmosphere of the grease-lubricated wagon axle bearing. In the case of water intake, the humidity of the gaseous atmosphere above the grease increases and can be detected by the customized sensor concept Humidity Sensor in Axle Bearings (HSAB). As this sensor system has to be sufficiently robust, it must be able to withstand environmental impact factors. The most important of these factors are temperature, relative humidity, and mechanical load, like vibrations and shocks, depending on the relevant railway application. To mimic these field effects under controlled laboratory conditions, the “lab-to-field” approach was set up and employed. Of the utmost importance was the installation of a development environment for the sensors that enabled the transfer of laboratory results to the respective rail field application. As a result, the HSAB system shows promise with respect to enhancing the reliability of railway wagons and decreasing maintenance costs, thereby reducing the downtime of railway wagons significantly.

Perbandingan Sistem Levitasi Magnetik Ems dan Eds Untuk Maglev Indonesia

Maglev trains are considered for urban transport and intercity transport systems. On low-medium speed Maglev trains, the operating routine is shorter than that of high-speed trains. Therefore, EMS and LIM technologies are preferred from the point of view of construction costs. However, in high-speed operation, EDS and NGO technologies are preferred for handling, and reliability. In addition, as the development of high-temperature superconductors and new types of permanent magnets develop, stronger and more cost-effective magnetic energy will be used for Maglev trains. The authors believe this technology can be utilized not only for railway applications but also aircraft launch systems and spacecraft launch systems. The need for a new and improved transportation system prompted many countries to be interested and seek to develop Maglev railways. However, although Maglev trains have been studied and developed for approximately half a century, few countries have the knowledge and expertise to do so. This review paper attempts to describe the current complete system in detail and summarizes the basic core technologies of Maglev trains from an electrical engineering point of view. The research hopes to be useful for people interested in this to assimilate Maglev train technology including magnetic levitation, propulsion, guidance, and power supply systems.

A real-time traffic rescheduling approach for the metro system

This paper addresses timetable disruptions in metro systems and their impact on passenger service quality. To mitigate these effects, the study introduces a time-space network-based traffic management approach with a two-layer network. The first layer focuses on timetable rescheduling, using an integer program for modeling and a Lagrangian relaxation-based heuristic for solution. The second layer addresses rolling stock duty rescheduling, employing a greedy heuristic for train set duties. The proposed approach is applied to a real-world problem presented in the INFORMS railway application competition (2022). Results demonstrate that, with topological vertex orders, the approach achieves schedule amendments within 3–6 minutes for various disruption scenarios. This rapid response highlights the method's efficacy and advantage in real-time applications, showcasing its potential for practical implementation in metro systems.

Application of Digital Twin Technology for the Digitization of Railway Maintenance Services in compliance with European Regulation EU 779/2019.

This paper delves into the application of Digital Twin Technology (DTT) in the digitalization of assets, processes, and services to improve maintenance performance in railway applications in the context of European Regulation EU 779/2019. Digitalization plays a pivotal role in certifying the suitability of rolling stock in the railway sector. The primary objective of this study is to meticulously examine the fundamental tasks required to build a digital twin in this specific context. The process involves the contributions of key entities fulfilling specific roles: the maintenance and asset certification service provider ensures the integrity and compliance of rolling stock; specialists in IoT and process automation, along with experts in software platforms and services, are tasked with implementing the technological aspects of the digital twin. As a result, a methodology is proposed for the integration of digital design that combines three approaches: Service approach, platform architecture, and model-based asset digitalization. This paper delineates critical stages, from data acquisition and management to model creation and information integration, highlighting the crucial role of DTT in this process. Furthermore, the challenge of digitalization in situations where assets and processes are in an initial state of low or non-existent digitalization is underscored. A profound understanding of digital assets, information layers, and underlying models becomes crucial in the context of the current challenges of railway digitalization.

Real-Time Resonance Detection and Active Damping in Energy Recovery Railways Applications

Real-Time Resonance Detection and Active Damping in Energy Recovery Railways Applications

Power Device Packaging Technology for Next Generation Railway Applications

Power Device Packaging Technology for Next Generation Railway Applications