Nadhir Messai

Design and Identification of Stochastic and Deterministic Stochastic Petri Nets

In this paper, we consider the identification problem of stochastic and deterministic stochastic Petri nets (PNs). The approach herein proposed consists of inferring a PN structure and identifying its parameters. Hence, the first step leads to the synthesis of a PN structure with the measurable sequence of events and states. This approach determines the measurable part and estimates the nonmeasurable part of the PN to be established. Once both parts are obtained, the PN structure and the initial marking of the nonmeasurable places are obtained thanks to the integer linear programming technique. In the second step of this approach, the parameters of the obtained model are estimated. Stochastic and deterministic stochastic PNs with deterministic and exponentially distributed transition durations are considered. A systematic identification method is proposed based on event sequences that are recorded by supervision systems. This method is based on a Markov model whose state space is isomorphic to the reachability graph of the untimed PN model.

Using Neural Networks for the Identification of a Class of Hybrid Dynamic Systems

Abstract This paper addresses the problem of the identification of Hybrid Dynamic System (HDS) by focusing the attention on the identification of a global model that predicts the continuous outputs of the HDS. The proposed approach considers the identification of HDS in terms of the architectures and the learning algorithms developed for Feed-Forward neural networks.

FAULT DETECTION FOR HDS BY MEANS OF NEURAL NETWORKS: APPLICATION TO TWO TANKS HYDRAULIC SYSTEM

Identification of Hybrid Dynamic System (HDS) is a challenging problem since it involves the estimation of different sets of parameters without knowing in advance which sections of the measured data correspond to the different modes of the system. This paper addresses such identification problem, by focusing the attention on the identification of a global model that predicts the continuous outputs of the HDS. In particular, we propose a methodology that permits to consider the identification of HDS in terms of the architectures and the learning algorithms developed for Feed-Forward neural networks.

Robust Fault Detection Based-Observer for Linear Switched Systems

Abstract This paper deals with the design of a robust hybrid observer for switched linear systems with unknown inputs and modeling error. The proposed observer is synthesized for the task of robust fault detection and concerns the case when the active mode is unknown. The basic idea of our approach is to formulate the design of the robust fault detection observer as a H∞ model-matching problem. Then, some results of H∞ optimization are exploited and an LMI solution procedure is proposed to synthesize the desired robust observer.

OFF-LINE IDENTIFICATION FOR A CLASS OF DISCRETE EVENT SYSTEMS USING SAFE PETRI NETS

Abstract This paper presents an identification method of partially observable Discrete Event System (DES) using safe Petri nets. This method allows constructing the formal model for a DES in a form of Petri net. The proposed method is based on the use of the input/output sequences constructed during the previous evolution of the system. The main advantage of this method is that priori knowledge are not needed and, a simple use of the measured Input/Output signals allows a complete identification of the system.

Graphic approach for the determination of the existence of sequences guaranteeing observability of switched linear systems

This paper deals with the design of sequences allowing the observability of the state of a switched linear system. The proposed method, based on a graph-theoretic approach, needs only the knowledge of the systems structure. The necessary and sufficient conditions for the existence of at least one switching sequence guaranteeing the observability of a switched structured system are provided. Such conditions are particularly intuitive and they can be implemented with classic algorithms of the graph theory.

Calibration and validation of a switched linear macroscopic traffic model

This paper deals with macroscopic traffic modelling. It investigates real magnetic sensor data, provided by the Performance Measurements System (PeMS), in order to identify some traffic parameters related to a portion of the SR60-E highway in Califorina. Then, these parameters are used to validate a switched linear traffic model.

Two neural network based strategies for the detection of a total instantaneous blockage of a sodium-cooled fast reactor

The total instantaneous blockage (TIB) of an assembly in the core of a sodium-cooled fast reactor (SFR) is investigated. Such incident could appear as an abnormal rise in temperature on the assemblies neighbouring the blockage. Its detection relies on a dataset of temperature measurements of the assemblies making up the core of the French Phenix Nuclear Reactor. The data are provided by the French Commission of Atomic and Alternatives Energies (CEA). Here, two strategies are proposed depending on whether the sensor measurement of the suspected assembly is reliable or not. The proposed methodology implements a time-lagged feed-forward neural (TLFFN) Network in order to predict the one-step-ahead temperature of a given assembly. The incident is declared if the difference between the predicted process and the actual one exceeds a threshold. In these simulated conditions, the method is efficient to detect small gradients as expected in reality.

A clustering-based approach for the identification of a class of temporally switched linear systems

The behaviours of hybrid dynamic systems (HDS) are determined by combining continuous variables with discrete switching logic. The identification of a HDS aims to find an accurate model of the systems dynamics based on its past inputs and outputs. In pattern recognition (PR) methods, each mode is represented by a set of similar patterns that form restricted regions in the feature space. These sets of patterns are called classes. A pattern is a vector built from past inputs and outputs. HDS identification is a challenging problem since it involves the estimation of different sets of parameters without knowing in advance which sections of the measured data correspond to the different modes of the system. Therefore, HDS identification can be achieved by combining two steps: clustering and parameter estimation. In the clustering step, the number of discrete modes (i.e., the classes that input-output data points belong) is estimated. The parameter estimation step finds the parameters of the models that govern the continuous dynamics in each mode. In this paper, an unsupervised PR method is proposed to achieve the clustering step of the identification of temporally switched linear HDS. The determination of the number of modes does not require prior information about the modes or their number.

A fault detection & isolation scheme for discrete time Switched Linear Systems

In this paper, hybrid observers based approach for faults detection and isolation, for a class of MIMO hybrid systems, is proposed. The faults herein considered can affect the sensors or the discrete trajectory of the considered system. The proposed approach is based on a Dedicated Switched Robust Observer Scheme (DSROS) using three blocks in interaction. A block for the mode signatures, a block for the fault signature a block for the diagnosis of the discrete events. Some LMIs conditions are provided to guarantee both of the robustness and the convergence of the proposed observers. Finally some simulation results illustrate the efficiency of the proposed approach.