Eric Gascard

Modular Modeling for the Diagnostic of Complex Discrete-Event Systems

For the complex systems, the development of a methodology of fault diagnosis is important. Indeed, for such systems, an efficient diagnosis contributes to the improvement of the availability, the growth of production, and, of course, the reduction of maintenance costs. It is a key action in the improvement of performance of industrial feature. This paper proposes a new approach to diagnose complex systems modeled by communicating timed automata. Each component has been modeled separately by a timed automaton integrating various operating modes while the communication between the various components is carried out by the control module. Starting from each module of the complex system, a single deterministic automaton, called a diagnoser, is constructed that uses observable events to detect the occurrence of a failure. This modeling formalism provides means for formal verification of the complex system model and its diagnoser. The model-checking methods are used to check correctness properties. The steps of the method are described by an algorithm and illustrated through a batch neutralization process. The implementation of the algorithm is also discussed.

Diagnostic and prognostic of hybrid dynamic systems: Modeling and RUL evaluation for two maintenance policies

In the industrial sector, maintenance plays a very important role in carrying out production by increasing system reliability and availability. Thee maintenance decision is based primarily on diagnostic modules, prognostics and decision support. Diagnostic consists of detection and isolation of faults, while prognostic consists of prediction of the remaining useful life of systems. Moreover, recent industrial systems are naturally hybrid: their dynamic behavior is both continuous and discrete. This paper presents an integrating architecture of diagnostic and prognostic in a hybrid dynamic system. Indeed, the diagnostic system is based on controlling task execution times during system operation. This method is based on a general modeling approach using hybrid automata. The model proposed is detailed by studying a two-tank system. To validate the model, a Stateflow controller is used. These failures are anticipated by a prognostics process based on a prediction of the remaining life for each component by taking maintenance policy into account. Two new methods are compared: ABAO (As Bad As Old) and AGAN (As Good As New), based on the type of repair strategy.

Performance Evaluation of Centralized Maintenance Workshop by Using Queuing Networks

Maintenance workshop integrated in the job-shop system is required in order to maintain the production machines, to ensure the continuity of service and thus to contribute to the improvement of the availability. The objective is to propose a maintenance workshop where all corrective maintenance activities are centralized. When a failed equipment from the production workshop occurs, it is sent to the maintenance workshop in order to be repaired with a given arrival rate. The aim is to maximize the operational availability of the production workshop by reducing the sojourn time in the maintenance workshop. This paper proposes a methodology to design the Central Maintenance Workshop, which enables us to evaluate the performance in terms of cost and sojourn time, for a given budget. For that, we propose a modeling framework based on the queuing network models. Simulation results are given in order to illustrate the influence of different parameters, like arrival rate of the failed equipments and the waiting time of the equipment.

Quantitative Analysis of Dynamic Fault Trees by means of Monte Carlo Simulations: Event-Driven Simulation Approach

Abstract The reliability analysis of complex and dynamic systems is often achieved by a quantitative analysis of dynamic fault trees (DFT), which model the system failure, i.e. a specific undesired event called top event, in terms of failures of the components of the system. Indeed, DFT takes into account the sequential relationships among events and their statistical dependencies. Given the failure probability of the components, the quantitative analysis aims at numerically evaluating, among other things, the failure probability of the top event. In this paper, we are interested in the Monte Carlo simulation which can consider any kind of failure distribution and is not limited in the DFT representation: it considers DFT with repeated events and shared events, takes into account all dynamic gates (PAND, SEQ, FDEP, and SPARE). However, Monte Carlo simulation encounters some disadvantages: an entirely new simulation must be executed every time a parameter changes and it may be time-consuming when the desired accuracy is high. To address these difficulties, this paper proposes a new dynamic fault tree simulation performed by an event-driven simulator. With this approach, gate simulations that produce no change in the output of a gate are eliminated augmenting the speed up of the simulation. The implementation of our approach uses an event queue data structure and an event-scheduler as alternative to the usual time-driven implementation which is characterized by an iterative loop. Thus, periods of inactivity are omitted. As results, computational efficiency is obtained and the speed-up performance of the Monte Carlo simulation program is improved.

A Polynomial Algorithm for Diagnosability Analysis of Discrete Event Systems

The paper deals with the definition of procedure that enables one to determine, for a given plant, if all faults can be detected and located after a finite sequence of observable events. More formally, the diagnosability is the property that every fault can be correctly detected from the observable events of the system after its occurrence no later than a bounded number of events. In this paper, the diagnosability problem of Discrete Event Systems DESs is studied. As modeling tool, finite-state automaton in an event-based framework is used. A necessary and sufficient condition of diagnosability of such systems is proposed. The results proposed in this paper allow checking the diagnosability of discrete event systems in an efficient way, i.e. in polynomial time.

Premiers pas vers le diagnostic de défaillances par exploitation d'un modèle SysML

Les systemes developpes actuellement evoluent continuellement pour repondre aux demandes d’une societe toujours plus exigeante. Ils ont tendance a se complexifier en alliant diverses technologies (mecanique, electronique, informatique). Dans ce contexte, le domaine de la surete de fonctionnement est de plus en plus sollicite pour que les systemes puissent repondre a des contraintes de qualite toujours plus elevees. Les methodes doivent s’adapter a la complexite de ces nouveaux systemes. Et le diagnostic de defaillances ne fait pas exception. Ainsi, certaines methodes de diagnostic se retrouvent confrontees a une difficulte importante : comment modeliser ces systemes complexes.Cet article presente nos premiers resultats sur l’exploitation du langage SysML pour le diagnostic de defaillances. Une methodologie de modelisation d’un systeme en langage SysML est proposee. Elle se veut generalisable pour etre utilisee dans differents domaines d’application. De plus, les premieres reflexions sur une methode de diagnostic de defaillances par exploitation d’un modele SysML sont developpees.

Automatic construction of diagnoser for complex discrete event systems

This paper deals with the problem of fault diagnosis of complex discrete event systems in the context of communicating timed automata. Indeed, for the diagnosis, this kind of systems can be represented by timed models whose components communicate through channels. This paper starts with a description of our modelling methodology of discrete event systems as communicating timed automata. The proposed approach for diagnosis (detection and isolation) is based on the methodology known as the diagnoser approach. This paper extends the approach of diagnoser through the taking into account of the various communicating synchronized automata representing the components of the system. It proposes an automatic step of construction of the global model. The application of the proposed algorithm allows to obtain the diagnoser of the studied system. Starting from a model of the complex system, this approach computes a deterministic automaton, called a diagnoser, which uses observable events to detect the occurrence of a failure. The different steps of the proposed method are described by algorithms and illustrated through a batch process.