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

2018 ◽  
Vol 233 (3) ◽  
pp. 455-469 ◽  
Author(s):  
Lei Jiang ◽  
Yiliu Liu ◽  
Xiaomin Wang ◽  
Mary Ann Lundteigen
Keyword(s):  
Control System ◽  
Bayesian Network ◽  
High Speed ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
High Speed Train ◽  
Train Control ◽  
Train Control System ◽  
Onboard System ◽  
Reliability And Availability

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.

Analisis Kinerja GSM-R Pada Kereta Cepat Indonesia

2020 ◽  
Vol 4 (2) ◽  
pp. 93-100
Author(s):  
Natriya Faisal
Keyword(s):  
New York ◽  
Control System ◽  
High Speed ◽  
Radio Waves ◽  
High Speed Train ◽  
New York University ◽  
Natural Conditions ◽  
Train Control ◽  
Train Control System ◽  
Railway System

The High-Speed Train of Indonesian will be implemented in 2022. The communication system used on the fast train is planned to use GSM-R in the future. GSM-R technology is expected to be applied to railways in Indonesia as well as in Europe. The railway system in Germany has applied GSM-R on the Berlin to Halle / Leipzig line using the first European Train Control System (ETCS) in Europe [2]. Radio waves used by GSM-R are at a frequency of 876 to 880 MHz for the uplink side and 921 to 925 MHz on the downlink side. This journal analyzes the GSM-R design that will be applied to Indonesian fast trains using the New York University Simulation (NYUSIM) method. This study evaluates the performance of GSM-R with intervention with natural conditions, such as temperature, humidity, air pressure, power and so on, resulting in a better GSM-R performance in terms of resistance to interference tested.

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

2017 ◽  
Vol 12 (4) ◽  
pp. 577 ◽  
Author(s):  
Dalin Zhang
Keyword(s):  
Big Data ◽  
Control System ◽  
High Speed ◽  
Sensor Data ◽  
Uncertain Reasoning ◽  
High Speed Train ◽  
Good Efficiency ◽  
Context Sensitive ◽  
Train Control ◽  
Train Control System

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.

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