High-speed Train Control System Research Articles

Virtual sample generation for model-based prognostics and health management of on-board high-speed train control system

In view of class imbalance in data-driven modeling for Prognostics and Health Management (PHM), existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment. A virtual sample generation solution based on Generative Adversarial Network (GAN) is proposed to overcome this shortcoming. Aiming at augmenting the sample classes with the imbalanced data problem, the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models. Under the PHM framework of the on-board train control system, the virtual sample generation principle and the detailed procedures are presented. With the enhanced class-balancing mechanism and the designed sample augmentation logic, the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status. Practical data from a specific type of on-board train control system is employed for the validation of the presented solution. The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance (CBM) operations.

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.

Constrained Spatial Adaptive Iterative Learning Control for Trajectory Tracking of High Speed Train

This paper proposes a constrain spatial adaptive iterative learning controller (CSAILC) for the displacement-speed trajectory tracking of automatic train control system with unknown parametric/nonparametric uncertainties and speed constraints. First, the nonlinear dynamic model of train operation is transformed from temporal domain into spatial domain utilizing a spatial state differentiator. Besides, the displacement-related parametric/nonparametric uncertainties are updated in the iteration axis. Furthermore, a barrier function is involved to satisfy the speed constraint, and the corresponding convergence analysis of the proposed CSAILC for automatic train control (ATC) is derived based on the spatial composite energy function. In addition, numerical simulations of train tracking control are carried out, and simulation results indicate that the proposed CSAILC achieves good effectiveness in a high-speed train (HST) control system.

Crucial States Estimation in Radio Block Center Handover Using Petri Nets With Unobservable Transitions

The radio block center (RBC) is one of the most essential ground systems in a high-speed train control system both in Europe and in China. The RBC handover procedure is an important function of RBC, which affects the transport efficiency, reliability and safety of railways. Analysis of crucial states in the RBC handover procedure is helpful to determine whether there are potential risks in the procedure, and to locate the fault in time when a fault occurs. In this paper, we study a property, called C-detectability, of the RBC handover. This property has been defined in discrete event systems and requires that the crucial states can be determined uniquely by observing the system output. Taking the RBC handover procedure in the Chinese train control system level 3 (CTCS-3) as an example, we first model the RBC handover procedure using Petri nets, which are a graphical and mathematical modeling tool to formalize the behavior of discrete event systems. Then, based on the notion of basis reachability graph, an efficient approach is used to check C-detectability of the Petri net modeling the handover procedure. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The railway system is a safety-critical system. System safety is essential to the railway system. It is necessary to provide formal methods to model the system and analyze its properties. The motivation of this work is to present a general modeling framework of railway systems for the crucial states estimation. The crucial states estimation of the railway system has not been discussed yet in the literature despite its importance due to its relationship with the safety of the railway. In particular, estimating the crucial states in the railway system helps to determine whether there are potential risks in the system. In this paper, we investigate a new state estimation property (C-detectability), which is related to fault diagnosis, marking estimation and fault identification. Future research will consider the use of timed Petri nets to increase the modeling power and to allow the performance analysis of the railway system.

Parallel anomaly detection algorithm for cybersecurity on the highspeed train control system.

With the rapid development of the high-speed train industry, the high-speed train control system has now been exposed to a complicated network environment full of dangers. This paper provides a speculative parallel data detection algorithm to rapidly detect the potential threats and ensure data transmission security in the railway network. At first, the structure of the high-speed train control data received by the railway control center was analyzed and divided tentatively into small chunks to eliminate the inside dependencies. Then the traditional threat detection algorithm based on deterministic finite automaton was reformed by the speculative parallel optimization so that the inline relationship's influences that affected the data detection order could be avoided. At last, the speculative parallel detection algorithm would inspect the divided data chunks on a distributed platform. With the help of both the speculative parallel technique and the distributed platform, the detection deficiency for train control data was improved significantly. The results showed that the proposed algorithm exhibited better performance and scalability when compared with the traditional, non-parallel detection method, and massive train control data could be inspected and processed promptly. Now it has been proved by practical use that the proposed algorithm was stable and reliable. Our local train control center was able to quickly detect the anomaly and make a fast response during the train control data transmission by adopting the proposed algorithm.

Research on Automatic Test Method of Radio Block Center Based on Script Technology

Radio Block Center (RBC) is the wayside core equipment of high-speed railway train control system, which is the key to achieve the safe operation of trains. In order to ensure the correctness of the function of RBC equipment and to send movement authority information to the train correctly, it is necessary to verify the consistency of RBC function with the RBC requirement specification. Therefore, the RBC functional testing is the basis and key to ensure the safety of train operation. The execution of testing cases has a great impact on testing efficiency. How to automatically execute testing cases and fully cover the system specifications is the key issue of RBC functional testing. Aiming at the shortcomings of traditional RBC functional testing, such as low efficiency, long time consuming, huge workload, and low accuracy of fault simulation, by studying the preparation of automatic test environment and the execution of automatic testing cases, this paper proposes a script based automatic functional testing method, and takes the RBC functional testing as an example to verify the correctness and effectiveness of this method. The practical application shows that the description ability of the test script language and the automatic testing environment meet the RBC testing requirements. The timeliness and the safety of RBC functional testing can be improved by using this method.

Model-based Simulation and Verification on Typical Function Scenes of High-Speed Maglev Vehicle Operation Control System

Under the new situation, high-speed maglev transportation has gradually become the new development direction of the railway field in the future. Its vehicle operation control system (VOCS for short) as a key subsystem has a great impact on the safety and stability of the entire high-speed maglev train control system. The high-speed maglev VOCS has the characteristics of complex system structure and frequent interaction between its modules. Finding hidden safety hazards and design errors in the system design and development process and ensuring the safety and efficiency of system design and development has become an important task of related projects. On this background, modelling and simulation of typical scenarios of high-speed maglev VOCS are absolutely necessary in the early stage of project development, drawing on the traditional wheel-rail vehicle system simulation verification research results, with the help of advanced computer simulation methods. Using Simulink / Stateflow tools for the parking point stepping functional scenario as an example, the validity and correctness of the high-speed maglev VOCS were verified to ensure the operation safety of the high-speed maglev train through.

Ontology-Based Event Modeling and High-Confidence Processing in IoT-Enabled High-Speed Train Control System

The rapid development of various types of real-time control systems raise new challenges on their heterogeneity and knowledge explicitly sharing issues. In this study, we propose an ontology-based model, named OntoEvent, to define and detect complex event in high-speed train control system. OntoEvent defines control logics using ontology structure and describes functionalities using logical, temporal operators and attribute relations. This ontology-based event processing model supports dynamic reconfiguration of functions and sharing between different components of the railway system. A pipelined construction framework is designed to transform OntoEvent model into semantic-consistent detection model. We implement a prototype control system, to evaluate the efficiency and performance of OntoEvent. Experimental results on this prototype system prove that OntoEvent-based event detection model outperforms other two selected models in results correctness, processing throughput and real-time performance, especially when processing a large amount of complex events.

A Method on Improving the Carrying Capacity for CTCS-3 Railway

Nowadays, the high-speed railway CTCS-3 train control system has realized the train tracking operation through the long block section consisting of two sections of track circuits. The train interval is composed of block sections, while the long block section will reduce the resolution of the train interval distance, which limits the carrying capacity of the railway to some extent. In order to reduce the capacity loss caused by long block section, without adding additional hardware equipment, a method to improve the carrying capacity of existing high-speed railway by dynamically combining block section was proposed in this paper. According to the different position of the two track circuits in the same block section, the block section is dynamically combined and the transmission code of the track circuit is dynamically adjusted to shorten the distance of the train tracking interval which improve the carrying capacity. Finally, taking the Beijing-Shanghai high-speed railway as an example, the theoretical analysis and calculation under different scenarios are carried out to verify the safety and effectiveness of the proposed method.

Research on the fault prediction method of an on-board subsystem in high-speed train control systems

The train control system is the “nerve center” of the high-speed train information system, which is large-scale and comprises various components. The on-board subsystem is the core of the train control system and is key to ensuring traffic safety and improving operating efficiency. Currently, the fault data processing methods of the on-board subsystem remain manual, which primarily realizes the fault detection and location. It is difficult to achieve the fault mechanism level, and fault prediction cannot be realized effectively. In this paper, the on-board subsystem structure and the fault disposal status were analyzed. The existing problems have been summarized, and some concepts and algorithms to predict faults were introduced. Based on the on-board subsystem structure and each modules performance parameters, the system-level fault prediction model was established. Based on the practical operational data sets, the fault prediction based on the Bayesian network was carried out and verified under 20, 200, 2000 and 20000 sets, respectively. The prediction accuracy was 5%, 27%, 92% and 96.3%, respectively, under the condition of 2000 data sets. The hidden Markov model and neural network-based fault prediction solutions were compared with the proposed method. The results demonstrate the advanced performance of the Bayesian network-based solution in system-level fault prediction.

Feasibility Study of Point Type Equipment for Detecting Train Transponder System

In the development of railway transportation, safety has always been the most concerned issue. Balise is the core content of high-speed railway train control system, it is one of the most basic unit of information storage and transmission, control and status information necessary for the train. It is affected by many factors in the process of operation, so it is unavoidable to have problems of malfunction. At present, staff are required to operate the transponder to repair or update message information. In order to meet the needs of daily maintenance, the scheme of reading and writing operation of transponder by dynamic train detection is proposed and its feasibility is analyzed. This paper designs the software part of the mobile detection system by analyzing the working status information of different versions of transponders and tests it. The test results show that the dynamic reading and writing operation scheme is feasible.

Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system.

In order to control the nonlinear high-speed train with high robustness, the fractional order control of nonlinear switching systems is studied. The fractional order controller is designed for a class of nonlinear switching systems by the fractional order backstepping method. In this paper, a simple and effective online updating scheme of model coefficients is proposed by using the flexibility of the model predictive control algorithm and its wide range of model accommodation. A stochastic discrete nonlinear state space model describing the mechanical behavior of a single particle in a high-speed train is constructed, and the maximum likelihood estimation of the parameters of a high-speed train is transformed into an optimization problem with great expectations. Finally, numerical comparison experiments of motion characters of two high-speed trains are given. The results show the effectiveness of the proposed identification method.

Operation-oriented reliability and availability evaluation for onboard high-speed train control system with dynamic Bayesian network

The reliability and availability of the onboard high-speed train control system are important to guarantee operational efficiency and railway safety. Failures occurring in the onboard system may result in serious accidents. In the analysis of the effects of failure, it is significant to consider the operation of an onboard system. This article presents a systemic approach to evaluate the reliability and availability for the onboard system based on dynamic Bayesian network, with taking into account dynamic failure behaviors, imperfect coverage factors, and temporal effects in the operational phase. The case studies are presented and compared for onboard systems with different redundant strategies, that is, the triple modular redundancy, hot spare double dual, and cold spare double dual. Dynamic fault trees of the three kinds of onboard system are constructed and mapped into dynamic Bayesian networks. The forward and backward inferences are conducted not only to evaluate the reliability and availability but also to recognize the vulnerabilities of the onboard systems. A sensitivity analysis is carried out for evaluating the effects of failure rates subject to uncertainties. To improve the reliability and availability, the recovery mechanism should be paid more attention. Finally, the proposed approach is validated with the field data from one railway bureau in China and some industrial impacts are provided.

High-speed Train Control System Big Data Analysis Based on the Fuzzy RDF model and Uncertain Reasoning

China high-speed train control system is a combination of computer, communication and control. Its events are diverse, including sensor data stream, GPS signal, GSM-R transmission data, real-time video monitoring data, train control software data, etc. These data have the typical characteristics of big data. If these data are well applied, this will be of great help to operations, maintenance, safety, passenger services, etc. This paper presents an efficient analysis method based on the fuzzy RDF model and uncertain reasoning for high-speed train control system big data. We have used the method proposed in this paper to analyze the data of the high-speed train control system. The experiment results show that the method proposed in this paper has good efficiency and scalability for the analysis of big data with different structures, types and context sensitive from high-speed train control system.

An extended system-theoretic hazard analysis method for the safety of high-speed railway train control systems

A high-speed railway train control system plays a key role in ensuring the safe operation of the trains. To ensure the safety of high-speed railway train control systems, it is vital to perform hazard analysis on them. In this context, this paper proposes a novel hazard analysis method which extends a previously reported system-theoretic hazard analysis method “system-theoretic process analysis.” The proposed method improves the standard system-theoretic process analysis to capture the temporal relations between inadequate control actions leading to hazards. These temporal relations are crucial for investigating the causes of hazards. To depict the temporal relations of control actions of high-speed railway train control systems, a new temporal logic called “control action temporal logic” is proposed first. Then based on this temporal logic, a new control action relation model is added into the process of system-theoretic process analysis. This model depicts the relations (including temporal relations) between control actions. In order to identify both the inadequate control actions and their temporal relations, an algorithm is designed and used. Finally, the effectiveness of the proposed method is verified through the hazard analysis on the Chinese Train Control System level 3.

中国高速铁路列控系统的形式化分析与验证

It is essential that the correctness of high-speed train control systems be guaranteed by formal verification because they are safety-critical. However, as these systems become increasingly more complicated, achieving this goal in practice is difficult, and may even be infeasible. On the other hand, it is more convenient to graphically model a complicated system. A graphical model is fairly intuitive, which has resulted in such models being used extensively in industry. Thus, to improve the reliability of a high-speed train control system, constructing a graphical model for the system and then detecting its bugs by simulation should be very effective. In this paper, we first show how to use Simulink/Stateflow to build graphical models for various combined scenarios of Chinese train control systems (CTCS), in which mode conversion and level upgrade take place simultaneously. This modeling approach can be easily adapted to model other scenarios in CTCS by simply modifying the corresponding parameters. Then, we analyze these graphical models via simulations and show that under some circumstances the trains will stop abnormally. Finally, in order to avoid the inherent incompleteness of simulation, we show how to translate these graphical models into formal models given in HCSP-a formal modeling language for hybrid systems that extends CSP-and subsequently formally prove that abnormal stops can happen in many of the cases in one of the combined scenarios. Formal verification of Simulink/Stateflow diagrams complements simulation and improves the reliability of systems being developed.

Hierarchical Modeling and Verification for High-speed Train Control Center by Time Automation

Chinese Train Control System level three (CTCS-3) is a major technical system in Chinese high-speed rail and Train Control System (TCC) is indispensable component in the CTCS-3. Current researches on TCC are mainly based on the simulation, which cannot ensure that all conditions in TCC are tested. This paper presents a hierarchical modeling method and uses time automation (TA) to model the TCC software. We take the design of the active balise telegram editing, a major part in the TCC software, as an example. At first, the process of the active balise telegram editing is analyzed to obtain a hierarchical diagram containing several layers. Then, TA is employed to build one TA model for each layer. Lastly, we use UPPAAL (a model validation tool, developed by Uppsala University and Aalborg University) to construct a network of the TA models to verify the active balise telegram editing. The verification results demonstrate that this modeling method is feasible and the model can meet the functional requirements of the TCC software.

Reliability Analysis for High-Speed Train Control System by Means of Stochastic Automata

Reliability Analysis for High-Speed Train Control System by Means of Stochastic Automata

Real-Time Optimal Speed Coordination and Scheduling for High-Speed Trains Based on Model Predictive Control

In the high-speed train control system, the command information such as allowable running distance, time and speed can be sent by the global system for mobile communications for railways (GSM-R). This paper will propose the framework of real-time train scheduling and control based on model predictive control for the optimal speed set-points of high-speed trains. The rolling optimization process combines the genetic algorithm with the simulation of train operation to evaluate the performance of speed set-points, which can be easily implemented in the parallel computing environment for real-time processing. The conflict resolution at the crossing stations is modeled by and embedded in the combination of various speed set-points which are formed from virtual to simulation speed. The final actual speed of train is engendered based on the movement authority and running time through the system of automatic train protection (ATP). The simulation results demonstrate the favorable performance of proposed method.

Coordinated Control among High-Speed Trains Based on Model Predictive Control

In the high-speed train control system, it is possible to realize the mutual real-time communication between trains and ground equipments, thus the real-time information about trains can be transmitted to the ground commanding center. Under this new operation paradigm, in order to improve its safety and efficiency, this paper proposes the generalized and hierarchical framework of model predictive control (MPC) for the railway system including macroscopic, mesoscopic and microscopic levels. Under this framework, this paper further elaborates the coordinated following control based on MPC among adjacent trains in order to guarantee proper safety distance in case of unexpected disturbances. The Levenberg-Marquardt optimization approach is utilized to engender the corresponding control commands. The simulation results demonstrate the efficiency and robustness of MPC with the prediction models of trains’ movement for the coordinated control among them.