Efficiency Of Train Operation Research Articles

A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems

A main circuit ground fault (MCGF) is a typical system fault in an electrical traction drive system (ETDS). When two or more MCGFs occur, it will cause serious accidents. Therefore, it is particularly important to detect and handle MCGFs in a timely manner. To improve the efficiency of train operation and ensure driving safety, this paper proposes a hybrid data-driven MCGF diagnosis method. First, the voltage signals related to the fault are selected according to the mechanism analysis of the MCGF, and then the initial feature variables are constructed according to these voltage signals. Secondly, the initial feature variables of different types of MCGF are analyzed in the time and frequency domains by wavelet transform, and four feature indicators are calculated. Finally, the fault feature indicators are trained by random forest to obtain a model for subsequent fault diagnosis. After comparative experiments using various machine learning methods, it was found that the RF used in the proposed method has a better diagnostic effect, and the correct isolation rate exceeds 99%.

Overview of knowledge graph construction in the field of fully electronic computer interlocking systems

As a core technology in railway signal control, the fully electronic computer interlocking system achieves the control of railway signals and switches through computerization, ensuring the safety and efficiency of train operations. With the increasing complexity of railway systems, traditional management and maintenance face significant challenges. Knowledge graphs, as an advanced data management and representation method, can effectively organize and utilize large-scale domain knowledge, enhancing the system's intelligence. This paper introduces the construction methods, core concepts, and applications of knowledge graphs in the field of fully electronic computer interlocking systems, providing references for research and practice in this area.

Enhanced algorithm for predictive maintenance to detect turbocharger overspeed in diesel engine rail vehicles

The reliability and safety of locomotives is crucial for efficient train operation. Repeated turbocharger failures in Israel Railways locomotive fleet have raised serious safety concerns. An investigation into the failures revealed that the uncontrolled acceleration and overspeed transients of the turbocharger shaft occurred before the failure. Early detection of potential turbocharger failures by predicting overspeed conditions is critical to the safety and reliability of locomotives. In this study, an enhanced novel algorithm for estimating the Instantaneous Angular Speed (IAS) of the turbocharger and diesel engines is presented to overcome the challenges of transient operating conditions of diesel engines. Using adaptive dephasing, the algorithm effectively isolates critical asynchronous vibration components that are crucial for the early detection of turbocharger failures. This algorithm is suitable for non-stationary speeds and is applicable to any range of rotational speed and rate of change. The algorithm requires the input of the basic parameters of the system, while all other parameters that control the process are determined automatically. The algorithm was developed specifically for the special operating conditions of diesel engines and improves predictive maintenance and operational reliability. The method is robust as it correlates between several characteristic frequencies of the rotating parts of the system. The algorithm was verified and validated with simulated and experimental data.

Research on key technologies of railway station codification digital twin

According to the requirements of railway transportation, to ensure that locomotives can receive frequency-shifted signal information through the track within the station, the track circuits must be codified. Due to the increased speed of train operations, braking has become more challenging, increasing the possibility of running a red signal compared to the present. Therefore, it is imperative to implement pre-overlay codification technology and equipment suitable for use in speed-enhanced sections. As the pre-overlay coding method must be adopted, it requires the isolation components in the interface equipment to have "fault-safe" performance. When the isolation component fails, the current or voltage introduced into the parallel track relay must not cause malfunction. Therefore, ensuring that the current and voltage of each component in the codified track meet the requirements is crucial. This research aims to establish a circuit simulation model based on MATLAB Simulink to achieve dynamic simulation of the pre-overlay ZPW-2000 codification circuit diagram of the 25Hz phase-sensitive track circuit in the electrified section. Through this model, the electrical characteristic indicators such as current, voltage, and waveform of each component on the circuit can be observed in real-time. This helps optimize the design and maintenance of the railway signal system, improving the safety and efficiency of train operations and contributing to the establishment of a digital twin model for the entire railway system's signal system.

Multi-scale attention network (MSAN) for track circuits fault diagnosis

As one of the three major outdoor components of the railroad signal system, the track circuit plays an important role in ensuring the safety and efficiency of train operation. Therefore, when a fault occurs, the cause of the fault needs to be found quickly and accurately and dealt with in a timely manner to avoid affecting the efficiency of train operation and the occurrence of safety accidents. This article proposes a fault diagnosis method based on multi-scale attention network, which uses Gramian Angular Field (GAF) to transform one-dimensional time series into two-dimensional images, making full use of the advantages of convolutional networks in processing image data. A new feature fusion training structure is designed to effectively train the model, fully extract features at different scales, and fusing spatial feature information through spatial attention mechanisms. Finally, experiments are conducted using real track circuit fault datasets, and the accuracy of fault diagnosis reaches 99.36%, and our model demonstrates better performance compared to classical and state-of-the-art models. And the ablation experiments verified that each module in the designed model plays a key role.

Determination of Parameters for Limiting Longitudinal Acceleration to Optimize Energy Consumption for Train Traction

Purpose. The main purpose of the article is to improve traction calculations using mathematical modeling when solving the equation of train motion. The train model is adapted to optimize the parameters of train movement during speed gain with the determination of functional relationships between the power of the locomotive power plant and the profile of the track section with the consumption of energy resources by the locomotive. Methodology. Modern methods of mathematical modeling were used to solve the tasks. The modeling of the power plant operation was performed using interpolation and approximation methods. The simulation modeling method was used to create a model of the electrical part of the diesel locomotive transmission. The method of integrating the differential equation of motion was used to calculate the parameters and nature of the train's movement and the problem was solved, which consists in finding a solution to this equation over the length of the traction section, taking into account the variable values of the locomotive power and the slopes of the track profile. Findings. To analyze the results of traction calculations, we obtained such indicators as technical speed and diesel fuel consumption, and compared these indicators. The results show that in the case of acceleration limitation, there is a decrease in diesel fuel consumption at almost unchanged technical speed. Originality. The improvement of energy efficiency of train operation has been further developed, namely, the functional dependence of the relative power of the locomotive power plant, which takes into account the actual profile of the site, has been obtained, which makes it possible to determine energy-saving modes of locomotive control and reduce energy consumption, load on the diesel engine and elements of the diesel locomotive traction transmission during transient modes, which, in turn, in addition to saving diesel fuel, will affect the reliability of the power plant elements. The practical value of the study is to save diesel fuel in the modes of movement that provide speed gain and increase the reliability of the power plant due to reduced load.

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Railway electrification has attracted substantial interest in recent years as a key part of the global effort to achieve transport decarbonization. To improve the energy efficiency of train operations, of particular interest is the optimization of train speed trajectories. However, most studies formulate the problem as a single-objective optimization model and do not take into account train mass uncertainty associated with the passenger load variations. This article formulates a biobjective robust optimization model to minimize both the energy consumption and journey time, in which the robustness against the uncertain train mass is considered and viewed as a decision-maker preference. A novel multiobjective optimization algorithm, namely, p-nondominated sorting genetic algorithm-II (NSGA-II), is proposed, incorporating the original NSGA-II and a proposed preference dominance criterion to handle the DM preference. With the proposed p-NSGA-II, not only all solutions will converge to the optimal Pareto front but also solutions with better robustness in the Pareto front will be automatically selected and retained; meanwhile, the spread of the selected solutions is maintained. The effectiveness of the p-NSGA-II to generate a set of performance-robust driving schemes is verified by numerical case studies. The results show that the p-NSGA-II can achieve up to 40.59% robustness improvement compared to the original NSGA-II.

An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree

High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable.

A reasoning diagram based method for fault diagnosis of railway point system

Railway Point System (RPS) is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations. For the fault diagnosis of RPS, most existing methods assume that sufficient samples of each failure mode are available, which may be unrealistic, especially for those modes of low occurrence frequency but with high risk. To address this issue, this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage. Specifically, the failure modes of RPS are distinguished through constructing a reasoning diagram, whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic. Then, an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem. Based on the results of decisions, the diagnostic rules are established to identify the failure modes. Finally, the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

Integration of Train Control and Online Rescheduling for High-Speed Railways in Case of Emergencies

The high-speed train control system is essential to the safety and efficiency of train operation. With the rapid increase of high-speed railway (HSR) operating mileage and development of information technology, the disposal flow and methods in emergency response are still based on dispatchers and drivers’ experience within the “layered” architecture of current system. There is a certain gap between current processing methods and effective resolution, which may even cause the spread of delay along with the railway networks. Therefore, we propose an integration system of operation control and online rescheduling to improve the recovery ability of HSR carrying capacity. We first describe the framework, information flow, and disposal process of the current system and analyze the shortcomings in handling emergencies. Then, the basic concept, system structure, and framework of the integration system are introduced. Finally, taking temporary speed restriction caused by strong wind as an example, we also analyze the principle of why and how the integration system can promote the recovery ability of HSR carrying capacity.

Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization

This paper proposes a novel train trajectory optimization approach for high-speed railways. We restrict our attention to single train operation scenarios with different scheduled/rescheduled running times aiming at generating optimal train recommended trajectories in real time, which can ensure punctuality and energy efficiency of train operation. A learning-based approach deep deterministic policy gradient (DDPG) is designed to generate optimal train trajectories based on the offline training from the interaction between the agent and the trajectory simulation environment. An allocating running time and selecting operation modes (ARTSOM) algorithm is proposed to improve train punctuality and give a series of discrete operation modes (full traction, cruising, coasting, full braking), and thus to produce a feasible training set for DDPG, which can speed up the training process. Numerical experiments show that an optimized speed profile can be generated by DDPG within seconds on a realistic railway line. In addition, the results demonstrate the generalization ability of trained DDPG in solving TTO problems with different running times and line conditions.

Research on Control Strategy of Heavy-Haul Train on Long and Steep Downgrades

The control of heavy-haul trains has always been the focus of China’s railway transportation development. One key challenge is the coordination of electric braking and air braking control when the electric-air combined braking is applied on long and steep downgrades. This is normally reliant on manual driving and thus is not cost-effective. To improve the safety and efficiency of train operation in existing heavy-haul railway lines, a multi-label random forest (ML-RF) based approach for heavy-haul train (HHT) operation is proposed. The control characteristics of electric braking and air braking on long and steep downgrades are analyzed first. A prediction model of control strategy is then established with the combination of line conditions and definition of multi-label learning. To evaluate the performance of the model, the 10-fold cross-validation method is adopted. Furthermore, a model parameter optimization algorithm based on evaluation metrics is designed. The feasibility of the proposed approach is demonstrated by the testing results on the actual train running data of one railway line.

Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

With the development of communication technology, train control operation system develops gradually, which significantly improves the reliability and efficiency of train operation. The current mobile Internet has gradually highlighted the many limitations of the mobile Internet in the high-speed mobile environment, which seriously deteriorate the service quality and user experience, and cause a waste of resources. In order to meet the real-time requirements of network communication resource scheduling in the mobile environment, aiming at the multidimensional dynamic adaptation framework constructed in a mobile environment, a service and network adaptation mechanism based on link failure state prediction is proposed in the paper. First, cross-layer theoretical analysis and actual data analysis are combined to construct a wireless link failure probability model. Then, reliable transmission requirements and transmission overhead are applied to optimize goals. Finally, simulation experiments are carried out according to the railway network data to evaluate the E-GCF adaptation algorithm. The experiment results show that compared with the current mainstream algorithms, the prediction accuracy of this adaptation algorithm is improved by 25%. The execution time of the algorithm is reduced by 9.6 seconds and the successful submission rate is as high as 99.99%. The advantages of the algorithm are significantly superior other algorithms. It proves that the research method of this paper can effectively improve the satisfaction rate and utility value of reliable transmission, as well as enhance the throughput performance. It solves the adaptation problems of frequent switching and low utilization of heterogeneous networks in a mobile environment, which contributes to the high-quality communication service of mobile network.

Real-time train regulation method for metro lines with substation peak power reduction

In high-frequency metro lines, train delays and substation peak power often occur, affecting safe and efficient train operation. In this paper, we propose real-time train regulation methods considering substation peak power reduction, in which runtimes and dwelltimes are adjusted to minimize the timetable and headway deviations and avoid multiple train accelerating. Firstly, we proposed two indirect indicators, i.e. overlapping time between accelerating phases and overlapping quantity between accelerating phases, which are minimized to suppress substation peak power in joint optimal train regulation models. The joint optimal train regulation models are based on the traditional real-time train regulation model considering the train traffic dynamics and control constraints. For the real-time requirement of train regulation, model predictive control (MPC) algorithms are designed to solve the formulated joint optimal control models, which generate the optimal train regulation strategies at each control cycle based on the real-time updated feedback system states. Finally, numerical examples based on one of the Guangzhou metro lines are implemented to verify the effectiveness and robustness of the proposed methods. The results show that the train regulation strategy with minimizing the overlapping quantity can not only suppress train delays and substation peak power, but also meet the real-time computation requirement.

Train Operations Organization in High-Speed Railway Station Considering Variable Configuration

The train operation problem at the railway station is one of the basic scheduling problems for railway companies, and the decomposition and combination of trains in station platform tracks is an important trend in the railway operation, which further increase the complexity of train routing problem in the high-speed railway station. A reasonable train routing plan is critical for ensuring the safety and efficiency of train operations within the stations. Considering the combination and decomposition process of trains at the station, a multi-objective model is constructed, which determines the receiving routes and departure routes of trains at the railway station with the limited infrastructure capacity, given the station yard layouts, train diagram and relevant technical operation time of the station. A solution framework based on the “constraint method” is designed, and software Gurobi is used to solve the problem. The Pareto optimal solution set is obtained. The results show that when making 1.9% concessions to the minimum route cost, the occupancy balance of platform tracks can be improved by 66.2%, which is taken as the comprehensive application scheme. Compared with the feasible scheme, the comprehensive operation scheme reduces the route cost by 1.9% and improves the occupation balance of platform tracks by 15.4%.

Adaptive Cooperative Tracking Control of Multiple Trains With Uncertain Dynamics and External Disturbances Based on Adjacent Information

Automatic train operation is an important part of the train control system. As train operating intervals continue to shorten and train speeds continue to increase, multiple train cooperative control is currently an important technology to further improve the efficiency of train operation and line passing capacity. However, considering various factors such as the nonlinearity and uncertainty of the train dynamics model and the complexity of the line conditions, this creates even greater demands on the design of the controller. In this study, we propose an adaptive cooperative tracking control method for multiple trains using adjacent information. For the multiple-train coordinated tracking control in the presence of model uncertainties, unknown parameters, and external disturbances, a distributed cooperative control scheme for multiple trains is designed using the displacement, velocity, and acceleration information of adjacent trains, combined with radial basis function neural networks and adaptive methods. A fast high-order sliding mode observer is used to estimate the train velocity and acceleration information. Stability and convergence are proved for single and multiple trains utilizing Lyapunov stability analysis. Simulation examples demonstrate the effectiveness of the proposed controller.

Train driver experience: A big data analysis of learning and retaining the new ERTMS system

Now and in the coming years a new European Rail Traffic Management System (ERTMS) is being deployed on a larger scale. In the system level chosen in the Netherlands (Level 2) lineside signals will disappear and the train driver will receive all information needed for safe and efficient train operation through an interface inside the train cab. Furthermore, procedures, driving skills and communication will alter. Many train drivers will thus need to be trained in driving with ERTMS. Train operating companies (TOC) have to decide on efficient and effective strategies to organise this extensive training volume and to reassure that drivers become and remain proficient after training when roll-out of ERTMS may not have been fully completed.In this paper the central questions to be answered are: How many hours/duties does a driver have to do during a certain period of time on an ERTMS track section to become proficient with ERTMS (learning period), and to stay proficient (retaining period)?The approach taken is unique for rail industry (to the authors' knowledge) and is based on big data analyses. In this study, we were able to study the collective performance of train drivers, by big data analysis while at the same time maintaining privacy through pseudonymisation of the train driver database and their individual performances, characteristics and other potential privacy sensitive data. Databases containing detailed information on all planned and accomplished train service times/positions of the last three years (2015–2017) of a large TOC were combined with personal data from HR databases (age, experience, ERTMS training history, job assessment results, etcetera). From the data, delays caused by rolling stock and train operating related issues were identified. The amount and duration of these delays were correlated with the personal data to test research hypotheses. Again, at the same time we maintained privacy of the individuals’ personal data.Although the research has some limitations due to the small amount of existing ERTMS lines, and associated rolling stock, the results show some clear correlations. Thus, quantifiable directions to the minimum amount of ERTMS driving per 3 months during the first year and in the years after are presented. The results are now being translated into models for training and rostering related to the phase of ERTMS roll-out.

Real-time Signal Light Detection based on Yolov5 for Railway

To improve the safety and efficiency of train operation, autonomous driving train have developed rapidly in recent years. Among them, the signal detection is one of the most basic functions. However, due to the small size of signal light and the complicated of the railway environment, the signal detection is still a huge problem. The existing methods, such as the approach based on Hough circle transformation, are hard to meet the practical application requirements. In this paper, a real time railway signal lights detection based on Yolov5 is introduced. And a lot of experiments were conducted to prove the effectiveness of the proposed method. The experimental results show that the proposed method achieved 0.972 for both average recall rate and average accuracy rate. Besides, the detection speed of the proposed method reached astonishing 100FPS. Overall, the detection speed and accuracy both meet the practical application requirements.

Study of Performance of RZhFA‑6500 Smoothing Reactors

DC traction power supply systems have become widespread in several countries. Electricity consumption by traction power supply systems has pronounced seasonal and daily peaks. The peak nature of consumption leads to additional heating of live parts since time constants of transient thermal and electrophysical processes differ by several orders of magnitude, therefore, reducing ohmic resistance of electrical equipment, other things being equal, is an extremely important task.Within the framework of solving the problem of increasing energy efficiency of equipment of DC traction substations, smoothing reactor filter devices with an armoured radial-cylindrical magnetic core with low ohmic resistance, named RZhFA‑6500, were developed and manufactured.In 2018, two blocks of RZhFA‑6500 smoothing reactors were put into pilot operation at one of the most heavily loaded sections of DC electrified railways, providing traffic of trains weighing up to 9000 tons and traffic volume of about a hundred pairs of trains per day.To determine comparative indicators of energy efficiency, data on consumption of electric power for traction of trains for the compared periods before and after introduction of smoothing reactors of a new type were requested. The data received from JSC Russian Railways contained information on the traffic volume, type of rolling stock, and weight of the transported goods in accordance with the executed traffic schedule through section under consideration. The resulting set of data served as the basis for development of a multivariate methodology for assessing energy efficiency of reactors of a new type, as well as their contribution to efficiency of train operation over the considered comparative time interval. Since efficiency of power equipment also depends on the ambient temperature, the average integral parameter of average daily temperature was introduced into the calculation methodology, which, as it turned out, during processing initial data and construction of characteristic graphical dependencies, has a comparative difference of less than one percent for the same monitored periods.Accurate quantitative accounting of the data on consumption of electrical energy provided for traction of trains, exchange of reactive energy with the supply network, volume of transportation allowed to obtain an objective comparison of contribution of smoothing reactors of a new type to increasing efficiency of train operation and reducing consumption of electrical energy for traction of trains.

Experimental–numerical investigation of momentary discomfort in a high-speed train in varying wind speed conditions

Understanding the effect of wind speed variation on wind-induced momentary discomfort is the basis to improve the train vibration comfort. This study focuses on this challenge. Firstly, the momentary discomfort evaluation index Pw is developed and proved by the full-scale test. Then, setting Pw as the critical value, a multi-body nonlinear numerical investigation of the effect of the wind speed change rate, peak-wind speed duration, and wind speed variation amplitude on momentary discomfort in a train is conducted. Furthermore, the unfavourable wind speed variation condition for the wind-induced momentary discomfort in the train is obtained. Results indicate that the most intense condition for the train vibration momentary discomfort appears when the change rate of wind speed is faster and the duration of the peak wind drives the car-body rolling to the limit position. Consequently, critical wind speed for the wind-induced momentary discomfort in changing wind speed conditions is proposed, which provides a reference for an efficient train operation scheme to improve vibration comfort in a train under wind speed variation conditions.