Antonio Ramírez-Treviño

Online Fault Diagnosis of Discrete Event Systems. A Petri Net-Based Approach

This paper is concerned with an online model-based fault diagnosis of discrete event systems. The model of the system is built using the interpreted Petri nets (IPN) formalism. The model includes the normal system states as well as all possible faulty states. Moreover, it assumes the general case when events and states are partially observed. One of the contributions of this work is a bottom-up modeling methodology. It describes the behavior of system elements using the required states variables and assigning a range to each state variable. Then, each state variable is represented by an IPN model, herein named module. Afterwards, using two composition operators over all the modules, a monolithic model for the whole system is derived. It is a very general modeling methodology that avoids tuning phases and the state combinatory found in finite state automata (FSA) approaches. Another contribution is a definition of diagnosability for IPN models built with the above methodology and a structural characterization of this property; polynomial algorithms for checking diagnosability of IPN are proposed, avoiding the reachability analysis of other approaches. The last contribution is a scheme for online diagnosis; it is based on the IPN model of the system and an efficient algorithm to detect and locate the faulty state. Note to Practitioners-The results proposed in this paper allow: 1) building discrete event system models in which faults may arise; 2) testing the diagnosability of the model; and 3) implementing an online diagnoser. The modeling methodology helps to conceive in a natural way the model from the description of the systems components leading to modules that are easily interconnected. The diagnosability test is stated as a linear programming problem which can be straightforward programmed. Finally, the algorithm for online diagnosis leads to an efficient procedure that monitors the systems outputs and handles the normal behavior model. This provides an opportune detection and location of faults occurring within the system

Observability of discrete event systems modeled by interpreted Petri nets

This paper is concerned with the analysis of the observability of the discrete event systems (DES) modeled by interpreted Petri nets (IPN). This paper presents three major contributions on the field of the observability of DES. First, an observability definition for IPN is proposed. This definition is more precise than previous ones because it deals with the possibility of determining the systems initial state, using the knowledge of the systems inputs, outputs, and structure. Later, a novel characterization of the IPN exhibiting the observability property that is based on the IPN structure is presented. Finally, a method for designing asymptotic observers is discussed. The main advantage over other methods is that the observer presented herein is given as an IPN, allowing further analysis of the system-observer pair.

Diagnosability of discrete event systems: a Petri net based approach

This work deals with model based fault diagnosis of discrete event systems. The model of the system, expressed as an interpreted Petri net (IPN) describes partially observed events and states, and includes all possible faulty states. Based on a modular modelling methodology, the input-output diagnosability property is introduced and structurally characterized. Then a diagnoser scheme is proposed allowing fault detection and location in polynomial time.

Structural Diagnosability of DES and Design of Reduced Petri Net Diagnosers

This paper deals with diagnosis of permanent and operational faults of partially observed discrete event systems modeled by interpreted Petri nets capturing both normal and faulty behaviors. Two main results are presented: a structural characterization of the diagnosability property and a method for designing reduced model diagnosers for online fault detection and location. Sufficient conditions for diagnosability are provided based on the analysis of the influence area of every fault fi in the model and the relative distance between pairs of transitions; polynomial algorithms are proposed for determining diagnosability. The diagnoser includes two reduced models that monitor the system; one for tracking the actual behavior and the other for establishing the expected behavior; the difference of markings in such models, called residue, provides enough information for the immediate location of faults, even if they occur simultaneously.

Petri net based fault diagnosis of discrete event systems

This paper deals with model based fault diagnosis of discrete event systems (DES). A methodology for building DES models using interpreted Petri nets (IPN) is presented; then the fault diagnosis problem is stated and the diagnosability property is defined. An algorithm for determining the current k - th post failure state of a partially measurable DES is proposed.

A modeling framework for urban traffic systems microscopic simulation

Abstract In this paper a modeling framework for urban traffic systems (UTS) is presented. The model, used for agent based micro-simulation, describes both the traffic network and dynamic entities, namely vehicles, traffic lights, and pedestrians. The framework allows defining systematically the necessary components and their behavior of a model oriented to event driven simulation, which can be executed in a distributed way. In the model, the vehicles are conceived as mobile agents with decision making capabilities that interact with the environment and other entities within the traffic network, performing diverse activities according to numerous situations arisen during the simulation. A multi-level Petri net based formalism, named n-LNS is used for describing the structure of the UTS and the components behavior. The first level describes the traffic network; the second level models the behavior of diverse road network users considered as agents, and the third level specifies detailed procedures performed by the agents, namely travel plans, tasks, etc.

On the Observability of Continuous-Time Switched Linear Systems Under Partially Unknown Inputs

This technical note addresses the problem of observability of continuous-time Switched Linear Systems (SLS) subject to unknown disturbances, unknown switching signals and unconstrained nonzero dwell time. For this class of hybrid systems, a geometric characterization of the observability properties is presented. In particular, attention is focused on the estimation of the SLS state from the continuous measurements. Using the well known concept of (A, B)-invariant subspaces, three main problems are addressed and solved, namely the observability for every nonzero state trajectory, the observability for almost every control input, and the observability for almost every state trajectory. It is also shown that the approach herein presented subsumes some of the classical results on observability of SLS previously reported in the literature, and allows expressing them in a common framework.

On the observer design problem for continuous-time switched linear systems with unknown switchings

Abstract The observer design problem for Switched Linear Systems ( SLS ) subject to an unknown switching signal is addressed in this work. Based on known observability results for SLS , an appropriate SLS observer is proposed and its convergence is analysed showing that the corresponding estimates converge in finite-time to the SLS state. More precisely, the observers of the continuous state evolution and the observers of the switching signal are investigated and their convergence studied separately. The main tool to analyse the observability is the well-known geometric concept of ( A , B )-invariant subspaces. The developed SLS observers are then applied to construct synchronized chaotic generators based on the SLS with chaotic behavior. Finally, an example of a non-trivial chaotic SLS and its detailed analysis are presented to illustrate the achieved results.

Observability in interpreted Petri nets using sequence invariants

This paper addresses the observability problem in discrete event systems modeled by interpreted Petri net (IPN). The concepts of input and output sequence invariants of an IPN are Introduced. These sequence invariants are used to state a characterization of observable IPN, which is similar to the one presented for continuous systems using a geometric approach. However, since the computation of sequence invariants is computationally hard, the characterization of observable IPN using sequence invariants is turned into a characterization based on the notions of event-detectability and marking-detectability.

Greenhouse Modeling Using Continuous Timed Petri Nets

This paper presents a continuous timed Petri nets (ContPNs) based greenhouse modeling methodology. The presented methodology is based on the definition of elementary ContPN modules which are designed to capture the components of a general energy and mass balance differential equation, like parts that are reducing or increasing variables, such as heat, CO2 concentration, and humidity. The semantics of ContPN is also extended in order to deal with variables depending on external greenhouse variables, such as solar radiation. Each external variable is represented by a place whose marking depends on an a priori known function, for instance, the solar radiation function of the greenhouse site, which can be obtained statistically. The modeling methodology is illustrated with a greenhouse modeling example.