Automatic Train Protection System Research Articles

A novel refined maintenance strategy for full life cycle of high‐speed automatic train protection system

AbstractAutomatic train protection (ATP) system is essential for ensuring the safe operation of high‐speed trains. However, the existing extensive and fixed maintenance mode of the ATP system results in a waste of resources. To achieve a state of operation and maintenance that ensures both protection capability and economic efficiency, a lean method in a dynamic maintenance mode for the full life cycle of the ATP system is proposed. Firstly, reliability tests are carried out based on historical failure data. The parameter values of the possible life distribution are estimated by maximum likelihood method, and the optimal life distribution of different devices is obtained through the Kolmogorov–Smirnov hypothesis test. Secondly, a dynamic failure rate function is introduced to describe the impact of maintenance on device performance. A refined maintenance model is then established within the life cycle, and the dynamically changing preventive maintenance intervals and frequencies are obtained using a genetic algorithm. Finally, to mitigate the impact of the intermittent operation of ATP system on maintenance, the multidimensional relationships among the maintenance strategy, service time and operation mileage are revealed. The effectiveness of the proposed method is verified through an example test on a type of driver machine interface device.

A real-time vital control module to increase capabilities of railway control systems in highly automated train operations

Recent advances in technology and railway have led to the introduction of systems and infrastructures capable of driving trains automatically. The Automatic Train Operation (ATO) system has been optimized for active human supervision. The next challenge is to realize ATO systems capable of achieving unsupervised operations on the mainlines. However, at this aim, additional safety functionalities should be provided. In this paper, we propose a pioneer hardware/software Vital Control Module (VCM) architecture capable of expanding the control capabilities of the existing train control system. The VCM includes a Printed Circuit Board (PCB), to be integrated into the cabin equipment, managed by a reliable and safe hard Real-Time Operating System (RTOS). Both hardware and software are developed to be compliant with related safety standards. The VCM integrates an application logic that acts as an on-board equipment control core, assessing the overall operativity in real-time, and promptly issuing emergency brakes if hazardous situations occur. The application logic has been developed with a model-based approach via Simulink/Stateflow tool and implemented as a C-script on the Xilinx Ultrascale + core housed on the PCB. We have used two testbeds to evaluate the VCM performance. Experimental results showed that the Worst-Case Response Time (WCRT) by the application logic is 13.6 times smaller than the most limiting specification-related deadline. The achieved earliness (− 1.8 ms out of 2 ms of deadline) allows for the easy expansion of VCM’s train protection capabilities in the future. Results from the second testbed showed that the VCM can intervene to mitigate hazardous situations ~ 2 times faster than the current automatic train protection systems according to the related standard.

Formal Verification of the European Train Control System (ETCS) for Better Energy Efficiency Using a Timed and Asynchronous Model

The ERTMS/ETCS is the newest automatic train protection system. This is a system that supports the driver in driving the train. It is currently being implemented throughout the European Union. This system’s latest specifications also provide additional functions to increase the energy efficiency of train driving in the form of ATO (automatic train operation). These functions of the ETCS will be valuable, provided they operate without failure. To achieve errorless configuration of the ETCS, a methodology for automatic system verification using the IMDS (Integrated Model of Distributed Systems) formalism and the temporal tool Dedan was applied. The main contribution is asynchronous and timed verification, which appropriately models the distributed nature of the ETCS and allows the designer not only to analyze time dependencies but also to define the range of train velocities in which the operational scenario is valid. Additionally, the novelties of the presented verification methodology are the graphical design of the system components and automated verification freeing the designer from using textual design. We express the verified properties as observer automata rather than in temporal logic. Moreover, we check partial properties related to system fragments, which is crucial in distributed systems. This paper presents the verification of an example ETCS system application. The verification results are presented as sequence diagrams leading to a correct/incorrect final state.

Residual risk in Automatic Train Protection systems: evaluation and management

The paper on typical rail operation hazards, such as Signal Passed at Danger (SPAD) and Failed Braking Application by Driver (FBAD). The protection against these hazards is normally by Automatic Train Protection (ATP) systems and emergency braking activation, operated by the onboard systems over a certain Minimum Release Speed (MRS). The determination of such speed is a key design parameter related with the achievable performances in terms of safety and capacity. The present research deal with a systematic analysis of the operational conditions potentially affecting the determination of such speed values by modelling the dynamics of the problem taking into account the features of the infrastructures (tracks layout, gradients, etc.), the vehicles (mass, braking performances, etc.) and the geography of signalling and ATP systems themselves. In fact, the position of main signals protecting the dangerous points (switches, level crossings, etc.) emerges as key player, affecting both the design and the operation of the systems.

A method of online anomaly perception and failure prediction for high-speed automatic train protection system

Automatic train protection (ATP) system is the key to ensure the safe operation of high-speed trains. However, the existing operation and maintenance mode for ATP systems cannot diagnose fault in time. In order to improve the protection capability of trains, this paper proposes an online anomaly perception and failure prediction method. First, with real-time operating data, an anomaly perception model based on long short-term memory network is established, where unstructured data are parsed into structured log keys and parameter vectors. It is trained with sequence matrices and its learning performance under different parameters is tested to find the optimal model. Experimental results show that the classification accuracy is 0.981, which is better than the existing methods. Then, with historical data, a failure prediction model based on time series is established, where one-dimensional time series of failure rate are reconstructed to high-dimensional space. The support vector regression method is used to fit the complex functional relationship between phase point and predicted point. And different algorithms are taken to find the optimal parameters. The results show that the model has the strongest generalization ability with the accuracy of 0.987. Finally, the intelligent operation and maintenance data service platform is designed.

Identification of High-Risk Driving Behavior and Sections for Rail Systems

Risk assessment is an important process for railway safety. Current practices for assessing the risks of driving behaviors aim to inspect the driving record generated by automatic train protection systems. This paper proposes an automatic process to access detailed data contained in driving data, and identifies six high-risk driving behaviors. The modules can assess the competency of drivers and evaluate the frequency of high-risk behaviors in each section. Moreover, an integrated risk index for driving behaviors is proposed to compare each driver and section. An empirical study for drivers and sections is performed to demonstrate the feasibility of applying the proposed modules in practice. Results reveal that 20% of high-risk drivers contribute to 74% of the total risk, while 15% of high-risk sections contribute to 80% of the total risk. The proposed modules identify the drivers and sections with high risk. By enabling the operators of railway systems to take countermeasures, this methodology could enable them to improve the safety of railway systems more efficiently.

TRAIN HEADWAY OPTIMISATION USING VIRTUAL BLOCKS

Although the implementation of a new Automatic Train Protection (ATP) system increases the safety level, it also exerts some impact on the blocking time in block sections, and consequently, on the headway between trains. At the same time, ATP systems introduce a train positioning system based on odometry calculation and reporting back to the trackside system. This paper describes the concept of using virtual blocks based on train position reporting in the ATP system for purposes of non-occupancy determination. Virtual blocks can be used to reduce headways on railway lines without increasing the number of trackside signalling devices. Preliminary capacity assessment was performed to calculate the average headway depending on the signalling system with reference to a case study.

An enhanced SHERPA (E-SHERPA) method for human reliability analysis in railway engineering

According to the European standard EN 50126 human reliability is a fundamental requirement in railway engineering. Notwithstanding that there are only few methods specifically developed for railway field. Trying to fill this gap, this paper proposes a human reliability analysis technique customized on railway applications to simulate a time-dependent model of the human error probability during the work shift. The first novelty of the proposed method is the use of customized task types for railway applications within a third-generation method. Furthermore, the concept of Eustress is considered to evaluate the impact of the stress on the operator performances by means of the Yerkes-Dodson law. The main novelty of the proposed method is the introduction of a simulator which provides the possibility of considering one or more breaks in the work shift allowing to simulate the error probability based on the duration of the break. The maintenance operations carried out on an Italian Automatic Train Protection System have been taken as case study to validate the proposed method in different scenarios. The results illustrate how a break during the working shift of the operator influences the probability of committing an error.

Research on Automatic Braking and Traction Control of High-speed Train Based on Neural Network

With the rapid development of economy and technology, people’s quality of life has been continuously improved, which also makes people put forward higher requirements for the quality of transportation. As far as railway transportation is concerned, the running speed and route of trains in the past gradually failed to meet people’s travel needs. Automatic train operation system (ATO), under the protection of automatic train protection system, realizes automatic train driving according to the instructions of automatic train supervision system, and can automatically control the starting, traction, cruising, idling and braking of trains. Traction drive system is the core part of high-speed train, and traction drive converter technology is one of the key technologies of high-speed train. In this paper, according to the different working mechanism of traction and braking, the dynamic model of train under traction and resistance braking is established by combining neural network algorithm, and the corresponding controller is designed by using nonlinear backstepping design method to realize automatic tracking of train speed and position.

EFFECT OF TRAIN POSITION REPORTING ON RAILWAY LINE CAPACITY

Over the recent years, an increase in the demand for passenger and freight rail transport services has been observed. This is mainly owing to the policy objectives related to the use eco-friendly means of transport and the investment causing the quality of rail services to grow. Along with the growing demand for rail transport, a problem of railway capacity utilisation has emerged. Although the implementation of the new Automatic Train Protection (ATP) system increases the safety level, but under some scenarios, it may decrease the headways between trains. At the same time, the ATP systems enables train positioning based on odometry calculation and reporting it to the trackside system. This paper proposes a solution that involves application of virtual block sections which use the train position reporting feature of the ATP system for the determination of non-occupancy. Virtual blocks can successfully reduce headways in railway lines without increasing the number of trackside signalling devices. The capacity assessment proposed under the study was performed to calculate the average headway depending on the signalling system configuration.

Analysis of Failure Features of High-Speed Automatic Train Protection System

An automatic train protection (ATP) system is the core to ensure operation safety of high-speed railway. At present, failure rate change rules of the system are not well understood and the maintenance strategy is not refined. In order to improve the protection capability and maintenance level of high-speed trains, this paper proposes a decision tree machine learning model for failure feature extraction of ATP systems. First, system type, mean operation mileage, mean service time, etc. are selected as ATP failure feature parameters, and cumulative failure rate is selected as a model output label. Second, support vector machine, AdaBoost, artificial neural networks and decision tree model are adopted to train and test practical failure data. Performance analysis shows that decision tree learning model has better generalization ability. The accuracy of 0.9761 is significantly greater than the other machine learning models. Therefore, it is most suitable for failure features analysis. Third, interpretability analysis reveals the quantitative relationship between system failure and features. Finally, an intelligent maintenance system for ATP systems is built, which realize the refined maintenance throughout life cycle.

Simulated and real train driving in a lineside automatic train protection (ATP) system environment

This paper presents the differences between the train traffic simulation tool RailSys and real train traffic with focus on Electrical Multiple Units (EMU) and the Swedish automatic train protection (ATP) system ATC, on a single-track line. Braking, acceleration, different types of resistance and running times have been investigated. From this study it can be concluded that the margins to unconditional ATC braking is high with this specific ATC implementation and parameters, and that the braking differs depending on the signal target. Compared to RailSys with parameters settings according to Swedish Transport Administration recommendations, the measurements show significantly lower acceleration and deceleration, which results in differences between measured and simulated running time.

Application of Fault Tree Analysis and Swiss Cheese Model to the Overspeed Derailment of Puyuma Train in Yilan, Taiwan

On October 21, 2018, a Puyuma express train went overspeed through a sharp curve and derailed in Yilan, Taiwan. This accident resulted in 18 fatalities and 267 injuries. Although such accidents occur once in a while worldwide, this case of an overspeed derailment from a train-set equipped with an automatic train protection (ATP) system (similar to the function of Positive Train Control (PTC) in the U.S.) is rare. A temporary investigation team was appointed by the Executive Yuan, the highest administrative organ in Taiwan, and the investigation was completed within 2 months. This paper presents the process, analysis, findings, and recommendations from the accident investigation. The accident was first analyzed using fault tree analysis to identify potential causes and contributing factors of this derailment. The results were then categorized into layers of defenses by using a Swiss cheese model. We further extended the original Swiss cheese model to a “time-dependent Swiss cheese model” to demonstrate how the barriers were penetrated at different times by incorporating the timestamps of important events. Another modified Swiss cheese model called “causal relationship Swiss cheese model” was presented to further demonstrate the causal relationships. With the proposed process and models, the immediate causes and contributing factors were quickly identified and presented in a way that could be easily understood by the general public. The results showed that the ATP system (or the PTC) cannot guarantee 100% safety. A review of the safety culture and corresponding procedures is important to ensure the safety of railway operations.

Inteligentne prognozowanie intensywności uszkodzeń automatycznego systemu ochrony pociągów kolei dużych prędkości w Chinach

Intelligent and personalized dynamic maintenance and spare parts configuration of high-speed railway have been the main trend to guarantee the safety capability of trains. In this paper, a new Automatic Train Protection (ATP) system failure rate calculation method is proposed, and the delay time and embedded dimension are determined by C-C algorithm. Then the phase space is reconstructed from one-dimensional time series to high-dimensional space. Based on chaotic characteristics of failure rate, a short-term intelligent forecasting model of failure rate of ATP system is established. The actual failure statistics from 2010 to 2018 are used as samples to train and test the validity of the model. From prediction results, it shows that the proposed chaos prediction model has an accuracy of 99.71%, which is better than the support vector machine model. Through the intelligent prediction of failure rate, this paper solves the maintenance inflexibility and imbalance of supply and demand of spare parts configuration.

Fault value analysis of on board ATP system using big data

RFM module is widely used for the classification of customer value. The classification results based on big data are often used as portrait of customer loyalty and customer value evaluation. In this paper, RFM model is introduced innovatively to analyse the fault of on board ATP (Automatic Train Protection) system. Through the fault value classification, it can effectively predict the impact of a certain type of fault on operation. For one thing, the operation departments and equipment providers can pre-process the corresponding failures according to the evaluation results, so as to reduce operational losses. For another thing, it can be used as the basis for dealing with big data of ATP faults.

A Train-Centric Communication-Based New Movement Authority Proposal for ETCS-2

Railway accidents inevitably happen around the world although the latest automatic train protection (ATP) or similar train control systems have been widely used. To increase the safety of train control systems, we propose a new train protection solution called movement authority plus (MA+). MA+ is designed to be overlaid onto a current train control system, e.g., European Train Control System Level 2 (ETCS-2), to form a parallel control system. Its system structure and working principles are presented. Redundant train-distance measurement strategies are adopted to ensure its availability. Safety performance of the overall system is evaluated and it is compared with ETCS-2. Benefit from the real-time information exchange between on-board MA+ units and that between them and switch announcement units, MA+ works based on the moving block principle. We calculate the minimum train headway distance required by MA+ and compare it with that required by ETCS-2. The results prove the feasibility of the proposed MA+ solution, i.e., MA+ is able to protect train safety when ETCS-2 fails, and would not interrupt its normal work.

Intelligent driving methods based on sparse LSSVM and ensemble CART algorithms for high-speed trains

• Intelligent driving methods (IDMs) are proposed to solve the multi-objective control problem of HST. • We integrate the expert knowledge system, sparse algorithm and ensemble CART algorithm to realize IDMs. • We propose dynamic allocated strategies of operation time to reduce the operation time error. • L0-norm minimization and ensemble CART algorithms can greatly enhance the generalization ability of proposed IDMs. • The proposed IDMs are better than traditional ATO controller and manual driving method. Currently, high-speed train (HST) is mainly controlled by manual driving and automatic train protection system, which may reduce the comfort of passengers and impair the intelligence of train operation. In recent years, some intelligent driving methods have been proposed for subway line. However, because of the continuous rise of HST’s operation speed and mileage, the driving data collected from HST is more than that of subway and the intelligent driving model will be complex if the source driving data is directly trained by data mining algorithms. So, the source driving data sets are classified into several classes in terms of the features of the driving data. In addition, iterative sparse L 0 -norm minimization is applied to sparsify the classified driving data and thus the redundant data will be deleted, which can speed up the computation speed of learning process. Furthermore, ensemble CART, including B-CART and A-CART are used to find the driving rules of both experienced drivers and ATO controller. Finally, the field data of Beijing-Shanghai high-speed railway and ATO simulation data are used to test the performance of the proposed intelligent driving methods. Compared with A-CART, the energy consumption, and the redundancy of the training data set of S-A-CART algorithm can be respectively decreased by 0.27% and 40% and the passengers’ riding comfort can be increased by 17.71%.

Wider aspects of deceleration supervision in ERTMS/ETCS

The paper concerns basic information about implementation of the ETCS system in the Czech Republic especially its influence on the railway operation. Also facts about the principles of the automatic train protection system with the full speed supervision are described as well as the specifics of the speed monitoring experiences. In the next chapter are some principles of the braking curves calculation.

An overview of the ETCS braking curves

The purpose of the article is to present an overview of the ETCS braking curves according to the European specifications. Though the ETCS specifications lay down the basic principles for the braking curves and the associated information displayed to the driver, but there is still no harmonized method to compute them. In the absence of any requirement, the algorithms of the ETCS on-board suppliers lead to different braking distances for a given type of rolling stock. For cross border trains, the differences through national rules require the implementation in the ETCS on-board of several national braking curves. The main features of the braking curves have been reviewed in the article, including the following main aspects: basic principle to ensure of the train movement safety, the purpose and main types of ETCS braking curves, the input parameters for braking curve calculation, construction of the emergency brake deceleration curve, and guaranteed emergency brake deceleration. The basic principle to ensure the train movement safety is based on their separation in fixed block distance (for a conventional signalling system) or in moving block-sections (for ERTMS/ETCS level 3). To ensure these principles the ETCS onboard computer must predict the decrease of the train speed in the future, from a mathematical model of the train braking dynamics and of the track characteristics ahead. This prediction of the speed decrease versus distance is called a braking curve. The minimum interval between trains under automatic train protection system is defined by minimum movement authority that comprises odometer tolerance, driver allowance, ATP reaction times, brake application time. Braking curve predict of the train speed decrease versus distance by the ERTMS/ETCS on-board equipment, from a mathematical model of the train braking dynamics and the track characteristics ahead. ETCS supervises both the position and speed of the train to ensure they continuously remain within the allowed speed and distance limits, and if necessary it will command the intervention of the braking system to avoid any risk of the train exceeding those limits. There have been considered in the work the main ETCS braking curves and the supervision limits of the EBD braking curves, the movement authority, the end of authority, the most restrictive speed profile, the supervised location and others. Differences in braking on dry and wet rails are considered. The braking on dry rails is relatively easy to represent through a statistical model that take into account the dispersion of the braking performance. But on wet rails the physical phenomenon that occur when braking are still today extremely difficult to model. In order to overcome this difficulty, two distinct rolling stock correction factors have been considered in order to get the guaranteed emergency brake deceleration.

Modelling and simulation for automatic train protection systems using an integrated algorithm

Automatic train protection (ATP) system is an automatic braking system for a train acting in excess of its prescribed speed. So far, the design and development of an ATP system are mainly based on the empirical formulae or frequent field tests, which causes a waste of expenses and brings some safety problems. Simulation technologies presently applied are not reasonable enough for ATP systems toward accurate performance results. And hence, it is difficult to provide a reference for the design and development of ATP systems. This paper proposes an integrated algorithm to model and simulate an ATP system on the LabVIEW platform. Comparisons between this simulation and actual operation data of an identical ATP system show that the simulation model based on the novel integrated algorithm perfectly fits the actual ATP systems. The proposed ATP simulation model properly provides a definitive reference for the design and development of ATP systems.