Fabien Belmonte

Interdisciplinary safety analysis of complex socio-technological systems based on the functional resonance accident model: An application to railway trafficsupervision

Abstract This paper presents an application of functional resonance accident models (FRAM) for the safety analysis of complex socio-technological systems, i.e. systems which include not only technological, but also human and organizational components. The supervision of certain industrial domains provides a good example of such systems, because although more and more actions for piloting installations are now automatized, there always remains a decision level (at least in the management of degraded modes) involving human behavior and organizations. The field of application of the study presented here is railway traffic supervision, using modern automatic train supervision (ATS) systems. Examples taken from railway traffic supervision illustrate the principal advantage of FRAM in comparison to classical safety analysis models, i.e. their ability to take into account technical as well as human and organizational aspects within a single model, thus allowing a true multidisciplinary cooperation between specialists from the different domains involved. A FRAM analysis is used to interpret experimental results obtained from a real ATS system linked to a railway simulator that places operators (experimental subjects) in simulated situations involving incidents. The first results show a significant dispersion in performances among different operators when detecting incidents. Some subsequent work in progress aims to make these “performance conditions” more homogeneous, mainly by ergonomic modifications. It is clear that the current human–machine interface (HMI) in ATS systems (a legacy of past technologies that used LED displays) has reached its limits and needs to be improved, for example, by highlighting the most pertinent information for a given situation (and, conversely, by removing irrelevant information likely to distract operators).

Safety Evidence Traceability: Problem Analysis and Model

[Context and motivation] Safety evidence plays an important role in gaining confidence in the safe operation of a system in a given context. For a large system, it is necessary to provide information about thousands of artefacts that might be used as evidence and about the relationships among themselves and also with other safety assurance assets. [Question/problem] Past research has only addressed some needs of traceability in safety-critical systems and thus has not provided a complete picture of safety evidence traceability. Lack of knowledge and awareness of these needs can result in poor evidence management and lead to certification risks. [Principal ideas/results] This paper aims to provide a broad overview of safety evidence traceability needs for practice and its associated challenges. We also propose a safety evidence traceability model, which has been validated with data from real-world critical systems. [Contribution] We discuss the motivation and challenges for safety evidence traceability, and present the various traces that need to be captured and maintained. This information can help researchers to shape future research based on industry needs and can help practitioners to gain a deeper understanding and a wider knowledge of safety evidence traceability, thereby facilitating safety assurance and certification.

A model based approach for safety analysis

This paper deals with model based safety engineering in Railway signaling systems development. Recently, model based system engineering (MBSE) has brought new specification means for large industrial system. Alstom Transport develops its own MBSE methodology supported by the SysML notation. In this context, a domain specific modeling language (DSML) has been developed for the safety studies enabling tight coupling with the MBSE environment. The paper describes a model to model translation. The translation developed takes the functional part of the system model and the dysfunctional viewpoint modeled within the safety DSML to generate an Altarica model of the system. The generated Altarica model is formal and allows, one from another, the dysfunctional simulation of the system and the generation of sequences of events leading to accidents.

Role of Supervision Systems in Railway Safety

This paper describes how the new generation of supervision systems in the railway industry can achieve operation from display process variables to all automated control where the human just monitors the automaton. In the railway specific industry, supervision is organized in switching zones and aims to be centralized in an Integrated Control Center. These centers implement integrated and computer based systems that perform train protection, train operation and supervision. Thus railway dispatchers using supervision have their tasks considerably simplified. Although considered today as not safety critical, railway supervision systems can contribute to safety in some scenarios where an appropriate decision of a supervision operator could notably reduce the severity of accidents. That is in particular the case for residual scenarios (intervention of maintenance teams on the tracks, manual operation of trains not protected by train protection system, coupling/uncoupling, emergency requiring the stopping and evacuation of a train etc) only covered by procedure, thus requiring human intervention by a person supposed correctly informed on the state of the system, thanks to the data provided by the supervision system.

Application of evidential networks in quantitative analysis of railway accidents

Currently, a high percentage of accidents in railway systems are accounted to human factors. As a consequence, safety engineers try to take into account this factor in risk assessment. However, human reliability data are very difficult to quantify, thus, qualitative methods are often used in railway system’s risk assessments. Modeling of human errors through probabilistic approaches has shown some limitation concerning the quantification of qualitative aspects of human factors. The proposed article presents an original method to account for the human factor by using evidential networks and fault tree analysis.

Role Of Supervision Systems In Railway Safety

New generation of supervision systems in industry can achieve operation from display process variables to all automated control where human is just monitoring automaton. In railway specific industry, supervision is organised in switching zones and aims to be centralised in an Integrated Control Centre. Such centres implements integrated and computer based systems that perform train protection, train operation and supervision. Thus railway dispatchers using supervision have their tasks considerably simplified. Although considered today as not safety critical, railway supervision systems can contribute to safety in some scenarios where an appropriate decision of a supervision operator could notably reduce the severity of accidents. That is in particular the case for residual scenarios (intervention of maintenance teams on the tracks, manual operation of trains not protected by train protection system, coupling / uncoupling, emergency requiring the stop and the evacuation of a train...) only covered by procedure, thus requiring a human intervention by a person supposed correctly informed on the state of the system, thanks to the data provided by the supervision system

Human reliability analysis for automatic train supervision

Abstract Although considered as not safety critical (safety functions being performed by other sub-systems), railway supervision systems can contribute to hazardous scenarios. It is of primary importance to identify this type of scenarios and evaluate their frequency. Although the train traffic operation centre remains the nerve centre where decisions are taken by human operators, this study takes into account human reliability and man-machine interaction reliability.

A Quantitative Study of the Occurrence of a Railway Accident Based on Belief Functions

In the field of railway systems, there is a great interest to include the human factor in the risk analysis process. Indeed, a great number of accidents are consider to be triggered by the human factors interacting in the situation. Several attempts have been made to include human factors in safety analysis, but they generally attack the problem in a qualitative way. The choice of qualitative methods arises from the difficulty to elicit human behavior and the effects on systems safety. This paper presents a first attempt to account for the human factor by using the generalized bayesian theory and fault tree analysis.

Dispatcher reliability analysis: SPICA-RAIL experiments

Although considered as not safety critical (safety function being performed by other sub-systems), railway supervision systems can contribute to hazardous scenarios. It is of primary importance to identify this type of scenarios and evaluate the behaviour of human operators. A state of the art in human reliability is presented. This article presents an experimental protocol based on an automatic train supervision system coupled to a traffic simulator. It allows to gather data for human reliability evaluation and man-machine performance study.