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A system for the asset management of railway trains is provided. It comprises a diagnosis system which includes a train surveillance system comprising sensors; at least one surveillance subsystem comprising sensors; a train autonomous agent, being a computer program; at least one subsystem autonomous agent, being a computer program; at least one communication network; wherein the train surveillance system, the at least one surveillance subsystem, the train autonomous agent and the at least one subsystem autonomous agent are nodes of the at least one communication network and are communicatively connected over the at least one communication network. The diagnosis system is connected to a fleet surveillance system on the wayside. Further, a respective method is provided.; L'invention concerne un système pour la gestion d'actifs de trains ferroviaires. Il comprend un système de diagnostic qui comporte un système de surveillance de train comprenant des capteurs ; au moins un sous-système de surveillance comprenant des capteurs ; un agent autonome de train, qui est un programme informatique ; au moins un agent autonome de sous-système, qui est un programme informatique ; au moins un réseau de communication ; le système de surveillance de train, ledit sous-système de surveillance, l'agent autonome de train et ledit agent autonome de sous-système étant des nœuds dudit réseau de communication et étant reliés de manière communicative sur ledit réseau de communication. Le système de diagnostic est relié à un système de surveillance de flotte sur le bord de route. L'invention concerne en outre un procédé respectif.

The optimization of maintenance strategies has become a key issue in the railway industry but also in most industrial fields. To address this challenge, many studies dealt with the estimation of optimal maintenance parameters. But what commonly happens when the degradation process suddenly changes? The operator has to face an unexpected, increasing number of severe defects (and then a strong drop of its availability). These changes are generally due to either: a new component, introduced in the system for obsolescence reasons; or changing operating conditions. Based on the dynamic Bayesian networks (DBN), formalism that has been proved relevant to perform reliability analysis can easily represent complex system behaviors. This paper introduces a dynamic maintenance strategy, able to detect these drifts and to evaluate their impacts on the rolling stock doors system’s behavior.

In most industrial fields, and particularly in the railway industry, the optimization of maintenance policies has become a key issue. Dynamic Bayesian networks (DBN) have been proved as relevant to perform reliability analysis as they can easily represent complex systems behaviors. Based on this formalism, graphical duration models (GDM) were developed by (Donat, et al., 2009) to set all kind of sojourn time distributions for each state of the system. Unlike to some Markovian approaches that impose exponential behavior, this approach could better model the exact degradation dynamic of real industrial systems. But, what commonly happens when the degradation process suddenly changes? The operator has to face with an unexpected increasing number of severe defects (and then a strong drop of its availability). These changes are generally due to either new component, introduced in the system for obsolescence reasons, or to changing operating conditions. The aim of the study introduced in this paper, focusing on Dynamic Maintenance Strategies, is to detect these drifts and to evaluate their impacts on the system’s behavior.

Considering availability purposes for train transportation, passenger accesses (doors and steps) are often designated as critical systems. To improve global availability of its rolling stock, Bombardier Transportation (BT) aims at reinforcing its maintenance procedure by introducing predictive diagnosis. The SURFER project has been initiated to develop online and in-cars tools to early detect and prevent faults. In this paper, an overview of achieved progress with respect to online predictive diagnosis will be introduced. For this purpose, many signals are recorded using a test bench by BT: electrical motor intensity current, door displacement, binary indicators as door closed and locked. The paper focuses on designing a semi-Supervised discriminative probabilistic model that take into account contextual variables (train inclination or constraints due to passengers affluence) to perform a robust predictive diagnosis. The main steps of the proposed method are the followings: the segmentation of the provided signals into opening and closing phases, the extraction of relevant features from opening/closing phases, the setting of the discriminative diagnosis model based on statistical semi-supervised learning. The proposed approach is tested on signals collected from regional trains fleeting around Paris. It allows the earlier detection of anomalies, for instance, those due to maladjustments. The practical implementation of this approach will be detailed together with its preliminary results.