João Carlos Basilio

Polynomial Time Verification of Decentralized Diagnosability of Discrete Event Systems

The first step in the diagnosis of failure occurrences in discrete event systems is the verification of the system diagnosability. Several works have addressed this problem using either diagnosers or verifiers for both centralized and decentralized architectures. In this technical note, we propose a new algorithm to verify decentralized diagnosability of discrete event systems. The proposed algorithm requires polynomial time in the number of states and events of the system and has lower computational complexity than all other methods found in the literature. In addition, it can also be applied to the centralized case.

Robust diagnosis of discrete event systems against intermittent loss of observations

In the usual approaches to fault diagnosis of discrete event systems it is assumed that not only all sensors work properly but also all information reported by sensors always reaches the diagnoser. Any bad sensor operation or communication failure between sensors and the diagnoser can be regarded as loss of observations of events initially assumed as observable. In such situations, it may be possible that either the diagnoser stands still or report some wrong information regarding the fault occurrence. In this paper we assume that intermittent loss of observations may occur and we propose an automaton model based on a new language operation (language dilation) that takes it into account. We refer to this problem as robust diagnosability against intermittent loss of observations (or simply robust diagnosability, where the context allows). We present a necessary and sufficient condition for robust diagnosability in terms of the language generated by the original automaton and propose two tests for robust language diagnosability, one that deploys diagnosers and another one that uses verifiers. We also extend the results to robust codiagnosability against intermittent loss of observations.

Robust codiagnosability of discrete event systems

We consider robust decentralized diagnosis of discrete event systems, where the goal is to detect the occurrence of unobservable fault events using a set of local diagnosers that are themselves subject to failures. We introduce a formal notion of robust decentralized diagnosability, called robust codiagnosability, and study its properties. Two different tests of robust codiagnosability are presented; one uses diagnoser automata and the other uses verifier automata. We also revisit the problem of centralized diagnosability and study the problem of diagnosability under partial observation, where the set of observable events is reduced; in this regard, we introduce the notions of partial diagnosers and indeterminate hidden cycles, which are subsequently used in the study of robust codiagnosability.

Brief paperRobust diagnosis of discrete-event systems against permanent loss of observations☆

We consider the problem of diagnosing the occurrence of a certain unobservable event of interest, the fault event, in the operation of a partially-observed discrete-event system subject to permanent loss of observations modeled by a finite-state automaton. Specifically, it is assumed that certain sensors for events that would a priori be observable may fail at the outset, thereby resulting in a loss of observable events; the diagnostic engine is not directly aware of such sensor failures. We explore a previous definition of robust diagnosability of a given fault event despite the possibility of permanent (and unknown a priori) loss of observations and present a polynomial time verification algorithm to verify robust diagnosability and a methodology to perform online diagnosis in this scenario using a set of partial diagnosers.

Computation of minimal event bases that ensure diagnosability

We deal with the problem of finding sets of observable events (event bases) that ensure language diagnosability of discrete-event systems modeled by finite state automata. We propose a methodology to obtain such event bases by exploiting the structure of the diagnoser automaton, and in particular of its indeterminate cycles. We use partial diagnosers, test diagnosers, and other new constructs to develop rules that guide the update of the observable event set towards achieving diagnosability. The contribution of this paper is the description of such rules and their integration into a set of algorithms that output minimal diagnosis bases.

A Petri Net Diagnoser for Discrete Event Systems Modeled by Finite State Automata

We propose in this paper a Petri net approach to online diagnosis of discrete event systems (DESs) modeled by finite state automata. The diagnosis method is based on the construction of a Petri net diagnoser (PND) which is constructed in polynomial time and requires less memory than other methods proposed in the literature. We also present methods for the conversion of the PND to both sequential function chart and ladder diagram for implementation on a programmable logic controller (PLC). Implementation issues are also addressed in the paper.

State-space parameter identification in a second control laboratory

A difficulty usually encountered in the preparation of a state-space-oriented control laboratory is that most of the system identification techniques available in the literature are for input/output models. Although in recent years there has been a growing interest in state-space identification methods, the application of these techniques in undergraduate courses are not immediate since they require a deep knowledge in mathematics and system theory. In this paper, experiments are proposed to estimate the parameters of a second-order state-space system, a dc motor-generator group, whose model plays a key role in a laboratory that deals with state-space design. The efficiency of the proposed experiments is demonstrated with the estimation of all parameters of a real system.

Generalized Robust Diagnosability of Discrete Event Systems

Abstract We address the problem of robust diagnosability of discrete event systems described by a class of automata, where each automaton in the class generates a distinct language. We introduce a new definition which generalizes all previous definitions of robust diagnosability; for this reason it is referred here to as generalized robust diagnosability. We also present a necessary and sufficient condition for the generalized robust diagnosability and propose a polynomial time algorithm for its verification.

Bridging the Gap Between Design and Implementation of Discrete-Event Controllers

Extended labeled Petri nets (ELPNs), i.e., labeled Petri nets with inhibitor arcs, are usually used to model the desired closed-loop behavior of a controlled discrete-event system, and, as such, their states are formed with both the controller and the plant states. However, the control logic is based on the controller states only and the interaction between controller and plant is carried out through sensor readings from the plant and control actions (forced events) from the controller. This makes ELPN not suitable for modeling the controller. Control interpreted Petri nets (CIPNs), on the other hand, include control actions in the places and sensor readings in the transitions as part of their formal structure, and so provide a better formalism for controller modeling. In this paper, we propose a two-step approach to discrete-event controller implementation, as follows: (i) we first propose a set of transformation rules to convert the initial ELPN to an equivalent CIPN, therefore extracting the control logic from the desired closed-loop behavior and (ii) we present a straightforward systematic way to translate the CIPN into a ladder diagram. We apply the results presented here to the implementation of the automation system of a plastic molding machine.

Robust diagnosability of discrete event systems subject to intermittent sensor failures

The modeling of physical systems using discrete event models assumes that a set of sensors always report the event occurrences correctly. However, bad sensor operation can result in loss of observability of the events associated with the malfunctioning sensors. If one or more sensors fail, it may be possible that either the diagnoser stands still or provide wrong information on the fault occurrence. This paper assumes that intermittent sensor failures may occur and deals with the problem of fault diagnosis in the presence of intermittent sensor failures. To this end, an automaton model for intermittent sensor failures based on a newly proposed language operation (language dilation) is presented in the paper. Necessary and sufficient conditions for robust diagnosability against intermittent sensor failure are also given in the paper. The development of a robust diagnoser that copes with intermittent sensor failures is another contribution of the paper.