Using Hybrid Automata for Diagnosis of Hybrid Dynamical Systems

Physical systems can fail. For this reason the problem of identifying and reacting to faults has received a large attention in the control and computer science communities. In this paper we study the fault diagnosis problem and modeling of Hybrid Dynamical Systems (HDS). Generally speaking, HDS is a system mixing continuous and discrete behaviors that cannot be faithfully modeled neither by using formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, because they combine the continous and discretes parts on the same structure. Hybrid automaton is a states-transitions graph, whose dynamic evolution is represented by discretes and continous steps alternations, also, continous evolution happens in the automaton apexes, while discrete evolution is realized by transitions crossing (arcs) of the graph. Their simulation presents many problems mainly the synchronisation between the two models. Stateflow, used to describe the discrete model, is co-ordinated with Matlab, used to describe the continuous model. This article is a description of a case study, which is a two tanks system

Détection et diagnostic des défaillances de Systèmes à événements discrets

National audienceCet article propose une solution sur la détection et le diagnostic des défaillances sur des systèmes de production dans divers secteurs d'activité. A partir du modèle de commande du système, nous construisons le(s) modèle(s) dynamique(s) en y intégrant le caractère temporel ; point fort de notre démarche. Dans la phase de modélisation, les automates temporisés ont été utilisés. Cet outil permet de prendre en compte la dynamique temporelle du procédé. La phase de simulation du modèle s'en suit en utilisant Matlab/Simulink/Stateflow. L'amélioration des performances et l'efficacité de notre modèle de surveillance développé, dépend de la démarche rigoureuse proposée. La dernière partie de notre travail demeure la plus importante et concerne la méthode mise en œuvre pour la localisation et l'identification des causes de ses défaillances

How to Develop Effective Maintenance Systems in Partnership with Production

International audienc

Supervision et surveillance par Bond Graph des systèmes industriels : application à un système de remplissage

National audienceDans ce papier, nous nous intéressons à la surveillance d'un système dynamique dans le but de contribuer à la mise en place d'une stratégie de Surveillance, permettant le suivi d'évolution de l'état des entités du système afin de localiser les éléments défaillants. Notre contribution réside dans l'utilisation d'une méthode de diagnostic et de localisation des défaillances en se basant sur la technique de la logique floue à partir d'un modèle Bond Graph du procédé

Backward time analysis for the diagnosis of discrete event systems

6 pagesInternational audienceThis paper deals with the diagnosis problem of discrete-event systems The proposed method is based on the model-checking technique, thanks to the time analysis of the dynamic model. A dynamic model with temporal transitions is proposed in order to found the origin of all possible faults. By "dynamical model", we mean an extension of timed automata for which the faulty states are identified. The model of the studied system contains the faultless functioning states and all the faulty states. Our method is based on the backward exploitation of the dynamic model, where all possible reverse paths are searched. The diagnosis method is based on the coherence between the occurrence time of the fault and the reverse path length. A simple real-world batch process is used to demonstrate the modeling steps and the proposed backward time analysis method to reach the diagnosis results

Implementation of a cost optimization algorithm in a context of distributed maintenance

International audienceThis paper concerns the application of the maintenance task scheduling approach in a distributed context. We are particularly interested in the case where there is, on the one hand, a central maintenance workshop (CMW) in which therepair cycles are carried out and, on the other hand, a mobile maintenance workshop (MMW) for repair by replacement at several sites according to a predefined schedule. The maintenance costs are then partly linked to the costs of spare parts, travel, repair or replacement times. A maintenance cost optimization methodology based on the use of meta-heuristics is proposed in this article. A comparative study was carried out using two optimization algorithms: the genetic algorithm and the simulated annealing. The algorithms were applied to a company in the Rhône-Alpes region

Timed automata for monitoring manufacturing systems

International audienc

Exploitation of Built in test for diagnosis by using Dynamic Fault Trees: Implementation in Matlab Simulink

International audienceThis paper presents the purpose of Dynamic Fault Tree (DFT) in Matlab Simulink in order to diagnose discrete event systems. The aim is to filter false alarms in automated systems that feature dependencies. Traditional Fault Tree is a tool used for system diagnostics, but it is limited because it uses traditional logic gates (OR, AND) which do not take into consideration the time and the dependencies of automated systems. As a result, new gates have been created to make it possible to consider dynamic aspects of automata. They are called temporal and dynamic logic gates. Consequently a research work has been done upstream to describe all the functioning cases of each gate and to represent them into digital timing diagram. Then, in order to provide an easy way to built DFT and analyze automated system faults, traditional, temporal and dynamic gates have been programmed using the StateFlow library of Matlab Simulink and added to a new toolbox created especially in Simulink for the construction and the simulation of DFT

Fault diagnosis method for timed discrete-event systems: Application to autonomous electric vehicle

International audienceWith the explosion of new technologies, a new type of systems has been designed. Their characteristics make them more and more complex, because of the increasing number of components and the mix of technologies (electronic, mechanic, software). In the field of functional safety, this creates problems to apply the existing methods. In particular, the fault diagnosis can be long and difficult due to these methods, which are not adapted anymore. In this paper, a fault diagnosis method is proposed for timed discrete- event systems, based on a timed automaton as a model of the system. Two concepts are used: the failure detection using only some discrete characteristic times and the isolation with the fault signatures. At first every stage of the modeling are explained, through the example of an autonomous electric vehicle. Then the diagnosis method is described step by step on this example. The efficiency is finally discussed

Diagnostic of discrete event systems using timed automata in MATLAB SIMULINK

International audienceGenerally, diagnostic involves two interrelated phases: the detection and the localization of failures. The approach proposed in this paper is based on operating time. The method is applicable to any system whose dynamical evolution depends not only on the order of discrete events but also on their durations as in industrial processes. Diagnosis of faults is achieved through the implementation of a model observer based on timed automata. This observer called diagnoser makes it possible to detect and locate possible failures in real time. A failure is detected when an event is not reaching at the waited date, or if it last too long compared to its ongoing operations. Temporal knowledge of the process to be monitored is therefore essential. The pro-posed diagnoser is a monitoring tool that can detect, isolate and locate a fault in a system. The used method-ology is based on the timed automata. The presence of an error corresponds to the execution of a state defined as the defective controller. For the detection phase, parameter detectability is the ability to detect a fault in the system. For the localization phase, the isolation is a property that corresponds to the ability to isolate (locate) a fault. The diagnostic performance is quantified through two parameters: the detection time and isolation time