Model-Based Diagnosis of Multi-Track Level Crossing Plants

Asian Journal of Control

Toguyéni³

2017

Self Cite

In this paper, we develop an on‐the‐fly and incremental technique for fault diagnosis of discrete event systems modeled by labeled Petri nets, in order to tackle the combinatorial explosion problem. K‐diagnosability, diagnosability, Kmin (the minimum K ensuring diagnosability) and on‐line diagnosis are solved on the basis of the on‐the‐fly and incremental building of two structures, called respectively fault marking graph and fault marking set graph, in parallel. We build on existing results, namely those establishing necessary and sufficient conditions for diagnosability, but we bring mechanisms to make the checking of such conditions potentially more efficient. We show that, in general, analyzing or even building the whole reachability graph is unnecessary to analyze diagnosability and build an on‐line diagnoser. Our technique was implemented in a prototype tool called OF‐PENDA, and a railway level crossing benchmark is used to make a comparative discussion pertaining to efficiency in terms of time and memory relative to some existing approaches.

“…Either of these two faults can induce a train-car collision. For more about the background of the LC systems and development of the n-track LC benchmark, the reader can refer to [20].…”

Section: Level Crossing Benchmarkmentioning

confidence: 99%

On‐the‐Fly and Incremental Technique for Fault Diagnosis of Discrete Event Systems Modeled by Labeled Petri Nets

Liu

Asian Journal of Control

Toguyéni³

2017

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“…Accidents at railway level crossings (LXs) often give rise to serious material and human damage and seriously hamper railway safety reputation, although the majority of accidents are caused by road user violations. LX safety is one of the most critical issues in railways, which needs to be improved urgently (Ghazel, 2009;Mekki et al, 2012;Liu et al, 2016). In 2012, there were more than 118,000 LXs in the 28 countries of the European Union (E.U.)…”

Section: Literature Review On Level Crossing Safety Studymentioning

confidence: 99%

“…Accidents at European LXs account for about one-third of the entire railway accidents. They result in more than 300 deaths every year in Europe (Liu et al, 2016). In some European countries, accidents at LXs account for up to 50% of railway accidents (Ghazel & El-Koursi, 2014;Evans, 2011b).…”

Section: Literature Review On Level Crossing Safety Studymentioning

confidence: 99%

A risk assessment study on accidents at French level crossings using Bayesian belief networks

Liang

International Journal of Injury Control and Safety Promotion

2017

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Level crossing (LX) safety continues to be one of the most critical issues for railways despite an ever increasing focus on improving design and practices. In the present paper, a framework of probabilistic risk assessment and improvement decision based on Bayesian belief networks (PRAID-BBN) is proposed. The developed framework aims to analyse various impacting factors which may cause LX accidents, and quantify the contribution of these factors so as to identify the crucial factors which contribute most to the LX accidents. A detailed statistical analysis is first carried out based on the accident/incident data. A BBN risk model is established according to the statistical results. Then, we apply the PRAID-BBN framework on the basis of the accident/incident data provided by SNCF, the French national railway operator. The main outputs of our study are conducive to efficiently focusing on the effort/budget to make LXs safer.

“…Railway Level crossings (LXs) are potentially hazardous locations where trains, road vehicles and pedestrians move in close proximity to one another. LX safety remains one of the most critical issues for railways despite an ever-increasing focus on improving design and application practices [1,2]. Accidents at European LXs account for about one-third of the entire railway accidents and result in more than 300 deaths every year in Europe [2].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network Modeling Applied on Railway Level Crossing Safety

Liang

Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification

Cazier

et al. 2017

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Nowadays, railway operation is characterized by increasingly high speed and large transport capacity. Safety is the core issue in railway operation, and as witnessed by accident/incident statistics, railway level crossing (LX) safety is one of the most critical points in railways. In the present paper, the causal reasoning analysis of LX accidents is carried out based on Bayesian risk model. The causal reasoning analysis aims to investigate various influential factors which may cause LX accidents, and quantify the contribution of these factors so as to identify the crucial factors which contribute most to the accidents at LXs. A detailed statistical analysis is firstly carried out based on the accident/incident data. Then, a Bayesian risk model is established according to the causal relationships and statistical results. Based on the Bayesian risk model, the prediction of LX accident can be made through forward inference. Moreover, accident cause identification and influential factor evaluation can be performed through reverse inference. The main outputs of our study allow for providing improvement measures to reduce risk and lessen consequences related to LX accidents.