Train System Research Articles

Resource Requirements in a Burn Mass Casualty Event.

Burn mass casualty event occurrences are rare but will place significant burdens on any burn unit or healthcare system. Effective disaster preparedness plays a significant role in mitigating the aftermath of a burn mass casualty. The aim of this study was to assess the resource requirements during the initial two weeks of a burn mass casualty event. Eight patients in a burn mass casualty event were simulated using the Emergo Train System®. These simulated patients were matched with real historical patients treated in our burn centre, and their resource requirements were analysed. An average of eight staff is required to care for a patient per day along with almost 75 h of operating time (excluding anaesthesia and turnover time). A substantial quantity of consumables was used in the first two weeks. This study has demonstrated the substantial material consumption and staff requirements in the first two weeks of management in a burn mass casualty event. Such findings will offer valuable insight for disaster preparedness planning and resource management strategies.

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer guided by a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from microstructure of EEG signals, such as trained kernels and effect of each kernel on the detected stages, to macrostructures, such as transitions between stages. The proposed system demonstrated greater performance than prior studies and the system learned information consistent with expert knowledge.

The effect of fastener clip fatigue for high-speed railway on vehicle-track dynamic interaction: Numerical analysis and probabilistic evaluation

To investigate the vehicle-induced fatigue damage evolution of fastener clips and their impact on the vehicle-track (VT) system, a numerical dynamic model is established to achieve probabilistic evaluation. The “rain flow” counting method is adopted to describe the stress cycles of the hazard area of the clips, and the S-N curve is used to predict the fatigue life of the clips. The solution of the dynamic equation of motion is obtained by the time-domain Green's function method, which is an effective method to improve computational efficiency. A versatile iterative algorithm is adopted to solve the nonlinear system considering the damaged clips. In the probabilistic evaluation framework, a novel random field method is employed to depict the spatial variability of clips’ strength. The applicability of the model established in this work in aspects of expressing random damage of fasteners and evaluating the dynamic behavior of the system is demonstrated by different numerical examples. The results show that the dynamic responses of the train and track system significantly exacerbate as the number of train cycles increases, which is closely related to the variability and non-uniform damage of the fasteners. The probability of cumulative damage to the fasteners induced by the train follows a three-stage rule, and the fatigue damage of the non-uniform fastener system caused by the train is greater than that of the uniform situation. In addition, the short wave components of track irregularity will significantly exacerbate the damage to fasteners and therefore degrade the status of the railway line. As the driving speed increases, the damage to the fasteners also increases, but the impact of vehicle speed on the fastener damage is smaller than that of track irregularity, especially in the range of fatigue damage greater than 0.9. Therefore, maintaining the geometric smoothness of the track, controlling the uniformity of the mechanical properties of the fasteners, and reducing the driving speed are feasible measures to extend the service life of the fastener system, thereby ensuring the operation of high-speed trains.

Методика уточненного расчета давления в тормозных цилиндрах поезда с учетом влияния утечек сжатого воздуха, распределенных по длине тормозной магистрали

Objective: to develop a methodology for estimating the filling value of the brake cylinders of a freight train, taking into account the amount of air leakage distributed along the length of the brake line. As initial data for determining the pressure of the brake cylinders, use the pressure drop in the brake line along the length of the train, the amount of charging pressure in the head of the train and the discharge stage of the equalizing tank. Methods: experimental study of gas dynamic processes in key volumes of the braking system in the head and tail parts of the train, such as: brake line and brake cylinder. Approximation of experimental data obtained from the braking system of a real train with the derivation of generalized equations. Results: as a result of the research, refined mathematical models were obtained that allow determining the pressure in the brake cylinder of any car, as well as the average pressure in the brake cylinders along the length of the train, taking into account the amount of leakage of the charging pressure in the head of the train and the length of the train in the cars. Practical significance: The application of a new methodology for calculating the filling value of brake cylinders will allow taking into account the influence of the length of the brake line on the braking force of each train car. This will help to correct the equation of train movement and improve the accuracy of calculating the braking distance of freight trains, taking into account the technical condition of the braking network.

Система комплексной диагностики элементов высокоскоростного подвижного состава и инфраструктуры: анализ и внедрение

Objective: to consider the issue of integrated diagnostics of the undercarriage equipment of high-speed rolling stock and track infrastructure, to analyze the impact of operational factors on the technical condition of rolling stock, to identify the main problems and propose possible solutions, to consider the prospects of domestic development in the field of integrated diagnostics of rolling stock and infrastructure. Methods: analysis of design features and impact on train operation safety, criteria and parameters of running stability and passenger comfort of the undercarriage equipment of the high-speed electric train “Sapsan”, description of prerequisites and stages of creation, testing and operation of the system of continuous monitoring of parameters of the dynamic system “undercarriage and track” of electric trains “Sapsan”, analysis of the impact of the main operational factors on the technical condition of the rolling stock undercarriage equipment. Results: the key role of the undercarriage equipment of the Sapsan electric train in ensuring operational safety and passenger comfort has been determined. The necessity of creating and implementing a system for continuous monitoring of dynamic reactions in the system “undercarriage equipment - track infrastructure” has been identified. The results of experimental operation of the prototype of the system in the Sapsan electric trains are considered on the example of research of track sections with increased impact on the rolling stock and problems of running stability of the electric train. It is revealed that vertical accelerations of axlebox units with significant amplitudes allow to reveal vertical irregularities of the track up to 3 m long, and constant registration of increased lateral accelerations of the bogie frame shows the necessity of solving the issues related to the geometry of wheel-rail contact at sinuous movement of rolling stock in terms of conicity. The current state and prospects of development of domestic systems of complex diagnostics are determined. Practical importance: the considered diagnostic system allows to carry out complex analysis of problems of technical condition of undercarriage equipment and infrastructure, to determine interdependence and level of influence of operational factors, which is the way to increase reliability and fault tolerance of rolling stock, operational safety and passenger comfort. Its integration into the digital platform will increase the efficiency of rolling stock maintenance planning and enterprise resource management.

Evaluation of energy performance and environmental benefits for transcritical CO2 thermal system in high-speed trains

The conventional air conditioning system used in high-speed railways has limitations in heating performance and environmental impact, necessitating the exploration of alternative refrigerants. The transcritical CO2 system has gained popularity due to its exceptional heating efficiency and environmental benefits. This study aims to evaluate the energy and environmental potentials for transcritical CO2 systems in high-speed trains based on four-vertical-and-four-horizontal railway lines within different climatic regions of China. A high-fidelity transcritical CO2 system model was first developed and validated through experiments. Then, a comprehensive energy and environmental evaluation method was proposed and applied to the four-vertical-and-four-horizontal railway lines. The results indicated that the transcritical CO2 system had a superior heating performance and competitive cooling performance based on daily and annual operations. Furthermore, the transcritical CO2 system exhibited significant environmental advantages with lower total life cycle climate performance for all lines, with an average emissions reduction ratio of 17.6% and a maximum emissions reduction ratio of 26.98%. These findings highlight the competitive energy performance and outstanding environmental benefits offered by transcritical CO2 systems as promising alternatives to air conditioning systems in railway transportation.

Odour and indoor air quality hazards in railway cars: an Australian mixed methods case study

PurposeThis case study aimed to diagnose the cause(s) of a seasonal, and objectionable odour reported by travellers and drivers in the railway cars of Australian passenger trains. The research questions were to: (1) identify whether significant microbial colonisation was present within the air handling system of trains and causing the odours; to (2) identify other potential sources and; (3) remedial options for addressing the issue.MethodsA mixed-methods, action research design was used adopted. Sections of the heating, ventilation, and air conditioning (HVAC) systems from odour-affected trains were swabbed for bacteria and fungi and examined for evidence of wear, fatigue and damage on-site and off-site. Insulation foam material extracted from the walls of affected trains was also subjected to a chemical assessment following exposure to varying humidity and temperature conditions in a climate simulator. This was accompanied by a qualitative sensory characterisation.ResultsUpon exposure to a variety of simulated temperature and humidity combinations to recreate the odour, volatile chemical compounds released from the insulation foam by water were identified as its likely cause. In addition, a range of potentially serious pathogenic and odour-causing microbes were cultured from the HVAC systems, although it is considered unlikely that bacterial colonies were the odour source.ConclusionThe research has implications for the sanitising and maintenance policies for HVAC systems on public transport, especially when operating in humid environments. The sanitary imposition, especially in the wake of COVID-19 may be required to ensure the safety of the travelling public and drivers.

Challenges and facilitating factors in interorganizational knowledge acquisition: evidence from the Orange Line Metro Train System and Sustainable Bus Rapid Transit Corridor

PurposeThis paper aims to identify challenges and facilitating factors in interorganizational knowledge acquisition. For this purpose, the interorganizational settings of the Orange Line Metro Train System and Sustainable bus rapid transit (BRT) Corridor in Pakistan are examined.Design/methodology/approachThis study uses an exploratory multiple case study approach. The empirical data encompasses semi-structured interviews and archival documents. Within and cross-case analyses are used for analyzing the data.FindingsThe findings identify challenges such as time pressure, knowledge hiding, finding credible information sources, organizational red tape and facilitating factors such as clear objectives, individual interest and personal commitment, and revisiting the organizational culture and environment in which interorganizational knowledge acquisition takes place.Originality/valueBy examining knowledge acquisition in interorganizational projects, this study contributes to the literature on knowledge-based theory.

An artificial potential field approach for event-triggered cooperative control of multiple high-speed trains with free initial states

ABSTRACT In this paper, a multi-algorithm-fusion-based cooperative tracking control strategy is proposed for high-speed trains to achieve the control objectives of reference speed trajectory tracking and adjacent safety distance maintenance simultaneously. First, the multiple high-speed train (MHST) system is formulated as a multi-agent system (MAS) with a virtual leader. Second, a coordination controller that considers the dynamic tracking performance and input saturation is designed by combining the MAS leader-follower consensus algorithm and the improved artificial potential field (APF) method. In addition, within the train-to-train (T2T) communication network, an event-triggered mechanism is introduced in the continuous state transmission process to address the communication channel capacity protection limitation issue. Third, the closed-loop stability of the MHST system is ensured by the comprehensive analysis of a novel log-type Lyapunov function. Finally, the effectiveness of the proposed event-triggered cooperative tracking control strategy is verified through a numerical example of the MHST system on a typical line.

Comprehensive degree of nonlinearity in the dynamic response of high-speed Maglev train

The aim of this study is to propose a method for evaluating the comprehensive degree of nonlinearity in the dynamic response of high-speed maglev train system through mathematical modeling and numerical simulation. A model of a typical high-speed maglev vehicle is established based on the principles of maglev vehicle dynamics, and control models for the suspension magnet and the guide magnet are developed using principles from the electromagnetism and automatic control theory. The vibration responses of the vehicle system, including acceleration and electromagnetic forces are calculated using a fast numerical integration method. This calculation is performed for various control parameters and track harmonic excitations. The Hilbert-Huang transform (HHT) is adopted to decompose the response signal and derive the local mean frequency (amplitude) and instantaneous frequency (amplitude) of the response, based on which the Degree of Nonlinearity (DN) of the vibration response of a single component is obtained by calculating the deviation between the instantaneous frequency and the mean frequency. The Comprehensive Degree of Nonlinearity (CDN) of the vibration response in the maglev train system is proposed and defined by incorporating the DNs of the vertical and lateral vibrations of the carbody, bogie, suspension magnet and guide magnet, and by quantitatively evaluating them. The effects of the electromagnet control parameters and harmonic irregularities (wavelength and amplitude) of the track on the DN of each component and the CDN of the entire vehicle system are analyzed in detail. The analysis results show that the DN and CDN can demonstrate the extent of instability in the vehicle system and can be applied for safety warning on maglev vehicles.

Generalized autoencoder-based fault detection method for traction systems with performance degradation

Fault diagnosis of traction systems is important for the safety operation of high-speed trains. Long-term operation of the trains will degrade the performance of systems, which decreases the fault detection accuracy. To solve this problem, this paper proposes a fault detection method developed by a Generalized Autoencoder (GAE) for systems with performance degradation. The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation. Regardless of the probability distribution, it can handle any data, and the GAE has extremely high sensitivity in anomaly detection. Finally, the effectiveness of this method is verified through the Traction Drive Control System (TDCS) platform. At different performance degradation levels, our method’s experimental results are superior to traditional methods.

A Loss-Model-Based Efficiency Optimization Control Method for Induction Traction System of High-Speed Train Under Emergency Self-Propelled Mode

A Loss-Model-Based Efficiency Optimization Control Method for Induction Traction System of High-Speed Train Under Emergency Self-Propelled Mode

Fault-tolerant control of bullet train based on fuzzy adaptive control

Abstract For the trajectory tracking problem of a single-particle train dynamics model under the condition of partial loss of power in the train power unit due to actuator failure during the operation of the EMU, considering the situation where the system exists with uncertain dynamics from the model and unknown external disturbers, a control method based on fuzzy adaptive fault-tolerant control method for EMU is proposed. Under the framework of the backstepping method, the fuzzy system is used to approach the system function, and a fuzzy adaptive controller is designed. It is proved that the designed control law can enable the train system to track the given speed and displacement trajectory under partial power loss. Using the CRH3 train as the research object, the results show that the designed controller can complete the trajectory tracking control task.

E-COMMERCE CHATBOT USING ARTIFICIAL INTELLIGENCE

As we know that time is money, and each company needs to deploy an Artificially Intelligent system in order to interact with the customers. The same functionality is served by the Chatbot. It is a human designed but artificially trained system which is used to give an ease to the human interaction. By deploying this Chatbot to the website main purpose served would be to collect the database of maximum customers visiting our website every day. This database thus received would serve as the medium of interaction between the food outlet and the customers. Customers will also feel convenient in ordering with a more interactive platform rather than manual ordering. Thus, this will lead to generate an enhanced number of customers and thus we could lead to get details example phone number and email id of various customers and directly interact with them with a decreased human assistance. Keywords: Chatbot, AI-Artificial Intelligence, Machine Learning, Cloud System.

Dynamic Weighted Slow Feature Analysis-based Fault Detection for Running Gear Systems of High-speed Trains

Dynamic Weighted Slow Feature Analysis-based Fault Detection for Running Gear Systems of High-speed Trains

Coordinated Control of the Onboard and Wayside Energy Storage System of an Urban Rail Train Based on Rule Mining

There are three major challenges to the broad implementation of energy storage systems (ESSs) in urban rail transit: maximizing the absorption of regenerative braking power, enabling online global optimal control, and ensuring algorithm portability. To address these problems, a coordinated control framework between onboard and wayside ESSs is proposed in this study, and the related control strategy is obtained by transforming the global optimization problem into a combination of rule-based and local optimization problems. The design process of the strategy is divided into three parts: offline optimization, rule extraction, and local optimization. First, a genetic algorithm is used to optimize the charge/discharge threshold curves under typical operational conditions. Then, the rules of offline optimization results are obtained from an expert system, and the nonlinear rules are mined with local optimization methods. Finally, a coordinated strategy is presented based on the outcomes of the three parts. A power hardware-in-the-loop (PHIL) experimental platform based on RTLAB is built, and the above coordination strategy is verified based on data for the Beijing Metro Batong Line. The experimental results show that the strategy can effectively improve the energy-saving rate and reduce the regeneration failure rate substantially, with the effect being close to the offline global optimal solution. The algorithm proposed in this paper achieves near global optimal energy-saving optimization results with lower computational costs, and has strong portability, providing a good solution for the large-scale application of onboard and wayside energy storage coordination control.

Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neom Smart City

Smart cities like Neom require efficient and reliable transportation systems to support their vision of sustainable and interconnected urban environments. High-speed trains (HSTs) play a crucial role in connecting different areas of the city and facilitating seamless mobility. However, to ensure uninterrupted communication along the rail lines, advanced communication systems are essential to expand the coverage range of each base station (BS) while reducing the handover frequency. This paper presents the dual transceiver free space optical (FSO) communication system as a solution to achieve these objectives in the operational environment of HSTs in Neom city. Our channel model incorporates log-normal (LN) and gamma–gamma (GG) distributions to represent channel impairments and atmospheric turbulence in the city. Furthermore, we integrated the siding loop model, providing valuable insights into the system in real-world scenarios. To assess the system’s performance, we formulated the received signal-to-noise ratio (SNR) of the network under assumed fading conditions. Additionally, we analyzed the system’s bit error rate (BER) analytically and through Monte Carlo simulation. A comparative analysis with reconfigurable intelligent surfaces (RIS) and relay-assisted FSO communications shows the superior coverage area and efficiency of the dual transceiver model. A significant reduction of up to 76% and 99% in the number of required BSs compared to RIS and relay, respectively, is observed. This reduction leads to fewer handovers and lower capital expenditure (CAPEX) costs.

In-situ verification of a deep-learning-based larval identification system for the Pacific oyster Magallana gigas

Natural seedlings are collected for use at Pacific oyster (Magallana gigas) aquaculture sites in Japan. For seedling collection to be successful, aquaculture farmers identify target larvae through microscopic observation, using morphological features such as the shell height/shell length ratio, lateral asymmetry of the shell, and umbo development. To reduce the time and labor associated with larval identification, we developed a deep-learning-based Pacific oyster larvae identification system, which consisted of a tablet connecting a new photographic device to capture images for identification. The final accuracies of the trained identification system for the learning datasets for measuring accuracy were 82.5% and 80.5% for precision (P) and recall (R), respectively, for all sizes of larvae (shell height ≥100 µm). We verified our identification system at an oyster aquaculture site. The obtained accuracy in the verification was low for some samples (63.6%–100.0% and 41.9%–100.0% for P and R, respectively, for pre-attachment stage larvae), indicating a decrease in identification accuracy for the in-situ samples. The decrease likely occurred owing to the natural variability of larvae. We modified the learning dataset with high variation in oyster larval images and retrained the identification system. The accuracy of the retrained identification system was increased to 83.9%–100.0% for P and R and thus satisfied the requirements for practical application for pre-attachment stage larvae. For small-sized larvae, P was improved but R was relatively low, indicating that although the detection level of our identification model for oyster larvae is low, the accuracy of the detected oyster larvae is high.

Design and development of Rotational Magneto Abrasive Flow Finishing setup powered with machine learning tools for nanofinishing of cylindrical double camshaft followers

Surface finish and morphology play a vital role in the reliability and life of cam-roller or slider mechanisms, with improvement due to lesser contact pressure-based fatigue and failure as surface finish improves. An experimental setup using magnet-based abrasive finishing techniques is designed and developed to conduct studies on a double camshaft follower arrangement that resulted in less than 300 nm finish at an enhanced finishing rate. Finishing rate improves greatly as speed of rotation of the workpiece increases with relatively bigger abrasive particles while the best finish is obtained at lower speeds and particle sizes with progression of the finishing cycles. Subsequently, using the results as the training dataset, a machine learning system is developed to overcome the limitations of surface measurement techniques and human interventions, to predict outcomes like number of cycles, speed of rotation of workpiece, time to reach the desired finish and particle size to be used for the given material of workpiece, initial surface roughness and final desired surface finish. The predictions from the completely trained system are compared with the physics-based model and the real data and the error is found to be within 20 nm which has led to development of a highly reliable system.

Development of transition schemes and optimization measures between normal slab track and ladder-type sleeper track

Severe vibrations and deterioration of track system are often observed due to the sudden change in support stiffness between normal slab track (NST) and damping track. To improve the dynamic performance of both the train and track system when a train runs on the transition zone between the NST and the damping track, we focused on the ladder-type sleeper track (LS track) and developed several optimized transition schemes. Firstly, a train-LS track coupled dynamics mode is developed, and it was verified using field testing. Afterward, using the dynamic responses of train and track systems in the transition zone between the NST and the resilient-fastener slab track (RFS track) as a reference, the shortcomings of the traditional transition scheme are identified, and the optimal transition scheme is developed by comparison of the proposed multiple schemes. Finally, the device for suppressing track vibration (DSTV) is installed on the longitudinal sleeper to alleviate the vibration of the LS track and improve the overall stability of the vehicle and track system during the transition zone. The results indicate that the optimal transition scheme proposed in this study lead to significant improvements in the dynamic performance of both the vehicle and LS track.