Reliable decentralized failure diagnosis of discrete event systems using single-level inference

Recently, safe diagnosability of discrete event systems (DESs) was investigated by Paoli and Lafortune, which was viewed as the first necessary step of fault-tolerant supervision. In this paper, we consider the problem of safe diagnosability in the framework of stochastic discrete event systems (SDESs). We define the notion of safe diagnosability for stochastic automata, in which fault detection occurs before any given forbidden string in the failed mode of system is executed. The relationship between diagnosability and safe diagnosability for SDESs is analyzed. In particular, a necessary and sufficient condition for safe diagnosability of SDESs is presented by constructing the recognizer of illegal language and the safe diagnoser. Some examples are described to illustrate the results.

The problem of safe failure diagnosis in discrete event systems is addressed. Starting from the standard definition of diagnosability of discrete event systems, which deals with the problem of detecting the occurrence of an unobservable event using the available observations on the system, the problem of performing the detection before the system executes a forbidden string is introduced. For example, this constraint could be required to prevent local faults from developing into failures that could cause safety hazards. This idea results in a new language property for discrete event systems called safe diagnosability. Necessary and sufficient conditions to test this language property are presented. Moreover, the problem of explicitly taking into account safe diagnosability as a requirement in system design is discussed.

formula omitted]-diagnosability for active on-line diagnosis in discrete event systems

In the conventional framework for failure diagnosis of partially observed discrete event systems, it is assumed that, for each event, the corresponding output symbol is determined uniquely. However, this assumption does not hold in discrete event systems such as a mobile system, where an output symbol depends on not only an event but also a state at which the event occurs. In this paper, we model such discrete event systems by Mealy automata with nondeterministic output functions, and consider a decentralized failure diagnosis problem that requires any occurrence of a failure should be detected by at least one local diagnoser within a uniformly bounded number of steps. We present a necessary and sufficient condition for the existence of a decentralized diagnoser. We then present an algorithm for verifying this condition. Moreover, we present a method for synthesizing local online diagnosers.

This paper is about the fault diagnosis of discrete event systems (DES) under time constraints. The DES contains a series of tasks with well-defined time intervals for each, collected during repetitive runs of the system. The repetitive runs allowed us to calculate average function time for each task. The objective is to propose an algorithm that verifies the correct behavior of each individual task within the system. To illustrate the principal of the algorithm, we will apply it with two concrete examples. This algorithm is easy to implement and operate, and is very useful for all types of systems.

Diagnosability is an important property in the field of fault diagnosis. In this paper, a novel approach based on logical formula is proposed to verify diagnosability of Discrete event systems(DESs). CNFFSM is defined to represent a new model for DES. Each transition in DES can be described as a clause. According to CNF-FSM, we construct a CNF-diagnoser. Based on the resolution principle and CNF-diagnoser, an algorithm is presented to test whether the failure events can be detected or not in a finite number of observable events. Our algorithm can be applied in both off-line diagnosis and on-line diagnosis. Experimental results show that our algorithm can solve the diagnosability problem efficiently.

In this paper a new approach to fault diagnosis of discrete events systems modeled with Petri nets is presented. Fault events are associated to a subset of unobservable transitions of the net. Negative markings, called generalized marking, are used to estimate the explanations of the observed transitions. The generalized markings allow to cope with the problems related with the explosion of the state space estimation in a very efficient way in terms of memory usage. This feature makes the proposed approach suitable for the on-line fault diagnosis of discrete event systems.

Previously we have introduced an inference-based framework for decentralized decision-making, where inferencing over the ambiguities of the self and the others is used to issue decisions. In this setting, we previously introduced the notion of N-inference V-diagnosability to characterize the existence of a disjunctive decentralized diagnosis scheme so that any fault can be detected within bounded delay, using at most N-levels of inferencing, by one of the diagnosers. While the disjunctive scheme relies on one of the diagnosers making the failure decision, the dual conjunctive scheme relies on none of the diagnosers making the nonfailure decision. It is known that the two schemes are incomparable, and in another paper we extend our earlier work to provide a more general framework, introducing the notion of N-inference diagnosability, capturing both disjunctive and conjunctive schemes. The contribution of this paper is developing a method for verifying N-inference diagnosability.

We are interested in constructing monitors for the detection of confidential information flow in the context of partially observable discrete event systems. We focus on the case where the secret information is given as a regular language. We first characterize the set of observations allowing an attacker to infer the secret information. Further, based on the diagnosis of discrete event systems, we provide necessary and sufficient conditions under which detection and prediction of secret information flow can be ensured, and construct a monitor allowing an administrator to detect it.

Introduction to Discrete Event Systems is a comprehensive introduction to the field of discrete event systems, offering a breadth of coverage that makes the material accessible to readers of varied backgrounds. The book emphasizes a unified modeling framework that transcends specific application areas, linking the following topics in a coherent manner: language and automata theory, supervisory control, Petri net theory, Markov chains and queuing theory, discrete-event simulation, and concurrent estimation techniques. This edition includes recent research results pertaining to the diagnosis of discrete event systems, decentralized supervisory control, and interval-based timed automata and hybrid automata models.

Reverse twin plant for efficient diagnosability testing and optimizing

This paper addresses fault pattern diagnosis of discrete event systems, involving fault pattern detection and diagnosability. A fault pattern is modelled as a finite automaton whose accepted language is the objective to be diagnosed, representing the occurrence of complex or composite faults. A verifier for fault pattern detection based on the synchronous product of a system and a fault pattern is proposed. By removing all silent events, a silent closure is calculated based on the synchronous product, which offers computational advantages for systems that have a large number of silent events. An NSC/FSC pair verifier is then computed by taking the product of a normal silent closure and an accepted silent closure. By studying indeterminate cycles of the NSC/FSC pair verifier, necessary and sufficient verification conditions are established, asserting that a system is diagnosable with respect to a fault pattern if and only if there is no indeterminate cycle in the NSC/FSC pair verifier. It is shown that the proposed method requires polynomial time at most. Finally, a case study to illustrate the results is provided.

Diagnosability of discrete event systems was previously defined in terms of finite state machines by Sampath et al. Two algorithms of polynomial complexity in the number of states were proposed later for checking their diagnosability. In this paper, we present an algorithm of polynomial complexity in the number of nodes for computing a sufficient condition of diagnosability of discrete event systems modeled by Petri nets. In other words, our algorithm is more efficient than previous ones since no state enumeration is necessary. This gives us an advantage to solve large real-world problems. Our algorithm is formulated as a linear programming problem, which is well-known to be of polynomial complexity in the worst case. Examples are given in the paper to illustrate our approach.

The aim of diagnosis is to detect and isolate faults according to the observable traces generated by the system. This article concerns multi-steps diagnosis of discrete event systems modelled with ordinary Petri nets. The main contribution is to decide which sets of places must be considered for r-steps diagnosis of some fault transitions or groups of fault transitions. Conditions and algorithms are proposed that lead to sensor selection in order to design diagnosers in a systematic way without expanding the reachability graph of the Petri net models.

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.