Ernesto López-Mellado

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.

Incremental synthesis of Petri net models for identification of discrete event systems

This paper addresses the problem of online identification of discrete event systems (DES). A passive method for the progressive building of Petri net (PN) models from DES outputs evolution is presented. After introducing several concepts related with dynamical properties of DES, a learning algorithm that computes ordinary PN models according to the measurement of cyclic output streams is proposed. A procedure based on this algorithm can be on-line executed tracking the DES behavior from its output signals; the successive computed models tend progressively to represent the actual observed behavior.

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.

A Comparative Analysis of Recent Identification Approaches for Discrete-Event Systems

Analogous to the identification of continuous dynamical systems, identification of discrete-event systems (DESs) consists of determining the mathematical model that describes the behaviour of a given ill-known or eventually unknown system from the observation of the evolution of its inputs and outputs. First, the paper overviews identification approaches of DES found in the literature, and then it provides a comparative analysis of three recent and innovative contributions.

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.

Required event sequences for identification of Discrete Event Systems

In previous works was addressed the on-line identification problem. This problem consists in compute an Interpreted Petri Net (IPN) model in proportion as new input and/or output sequences of the system are observed. Now in this paper are presented the required transition sequences needed to identify an IPN model that describes the complete behavior of a DES. These transition sequences are important because if from the knowledge of the output signals of the system we can compute these kind of transition sequences, then it will be guaranteed that the complete behavior of the system is captured in the computed model, even not all transition sequences of the system have been detected.

A Self-Organization Algorithm for Robust Networking of Wireless Devices

This paper deals with distributed creation and maintaining of ad hoc networks including diverse wireless devices, namely sensors, PDA, cell phones, and video games consoles. A cluster-based self-organizing strategy is proposed for building a backbone among the mobile devices, detecting segmentation, and recovery. In this approach, each mobile device is controlled by a multi-role agent, which performs these tasks efficiently based only on local interactions; role management allows the backbone reconfiguration when the nodes leave or arrive to the network yielding a complex global emergent behavior. Energy saving is achieved by adapting the time interval and power of transmission after the network formation. Simulations showed that the distributed algorithms performance is close to that obtained by an optimizing global procedure.