Abstract

International audience; The modes of reasoning which are used in the context of safety analysis and the very nature of knowledge about safety mean that a conventional computing solution is unsuitable and the utilization of artificial intelligence techniques would seem to be more appropriate. Our research has involved three specific aspects of artificial intelligence: knowledge acquisition, machine learning and knowledge based systems (KBS). Development of the knowledge base in a KBS requires the use of knowledge acquisition techniques in order to collect, structure and formalizes knowledge. It has not been possible with knowledge acquisition to extract effectively some types of expert knowledge. Therefore, the use of knowledge acquisition in combination with machine learning appears to be a very promising solution. This paper presents the result of these two research activities which are involved in the methodology of safety analysis of guided rail transport systems.

Abstract

International audience; This article describes a contribution to improving the usual safety analysis methods used in the certification of railway transport systems. The methodology is based on the complementary and simultaneous use of knowledge acquisition and machine learning. We used the ACASYA software environment to support the safety analysis aid methodology. ACASYA aims to provide experts with suggestions of potential failures which have not been considered by the manufacturer and which are capable of jeopardizing the safety of a new rail transport system. ACASYA consists of two main modules: CLASCA and EVALSCA, respectively dedicated to the classification and evaluation of accident scenarios. CLASCA is an inductive, incremental and interactive learning system. EVALSCA, built around a learning system called CHARADE, aims to provide experts with suggestions of potential failures which have not been considered by the manufacturer and which are capable of jeopardizing the safety of a new rail transport system.