Decentralized Failure Diagnosis Research Articles

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

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

“Polynomial Time Verification of Decentralized Diagnosability of Discrete Event Systems” Versus “Decentralized Failure Diagnosis of Discrete Event Systems”: A Critical Appraisal

In [1] , the authors claim that there is an oversight in [2] , in the sense that the proposed verifier is, in general, nondeterministic and the computational complexity analysis is incorrect. The authors in [1] also claim that the complexity of the verification algorithm presented in [3] is reduced when considering the more restrictive setting of projection masks, in contrast to the more general non-projection masks case, and equals the complexity of the verification algorithm presented in [2] . In this note, we show that the computational complexity analysis of [2] is actually correct and that the complexity of the verification algorithm presented in [3] is not reduced without additional modification of the algorithm (not yet proposed in the literature) if projection masks are used, and, therefore, is not equal to the complexity of the algorithm presented in [2] .

Online Synthesis of Conjunctive Decentralized Diagnosers for Discrete Event Systems

In this paper, we consider a decentralized failure diagnosis problem for discrete event systems. For a conjunctively codiagnosable system, there exists a conjunctive decentralized diagnoser that can detect the occurrence of any failure within a uniformly bounded number of steps. We present a method for synthesizing such a conjunctive decentralized diagnoser as an online diagnoser.

Comments on “Polynomial Time Verification of Decentralized Diagnosability of Discrete Event Systems” versus “Decentralized Failure Diagnosis of Discrete Event Systems”: Complexity Clarification

An article on codiagnosability verification of discrete event systems was reported by Moreira et al, claiming an improvement in complexity over a paper by Qiu and Kumar. This note clarifies an oversight in the complexity analysis of Moreira et al's paper. Further the results of Moreira et al's paper were obtained in a more restricted setting of “projection masks”, in contrast to the more general “non-projection masks” allowed in Qiu and Kumar's paper, which was overlooked. Finally in the special case when the projection masks are used, the complexity of Qiu and Kumar's paper is lower compared to the non-projection masks case, and equals the corrected complexity of Moreira et al's paper.

Reliable Decentralized Failure Diagnosis of Discrete Event Systems

In most existing works on decentralized diagnosis of discrete event systems, it is implicitly assumed that diagnosis decisions of all local diagnosers are available to detect the failure. However, it may be possible that some local diagnosis decisions are not available due to some causes. Letting n be the number of local diagnosers, the notion of (n,k)-reliable codiagnosability guarantees that any occurrence of the failure can be detected by using arbitrary more than or equal to k local diagnosis decisions within a uniformly bounded number of steps. In other words, even if at most n-k local diagnosis decisions are not available, the failure can be detected by using the remaining diagnosis decisions. In this paper, a method for verifying (n,k)-reliable codiagnosability for any k is presented. Then, the delay bound within which any occurrence of the failure can be detected by using arbitrary more than or equal to k local diagnosis decisions is computed.

Verification of Codiagnosability for Discrete Event Systems Modeled by Mealy Automata With Nondeterministic Output Functions

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 technical note, 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 introduce an extended version of codiagnosability as a condition for the existence of a decentralized diagnoser. We then present an algorithm for verifying the extended codiagnosability condition.

Decentralized failure diagnosis of discrete event systems

By decentralized diagnosis we mean diagnosis using multiple diagnosers, each possessing its own set of sensors, without involving any communication among diagnosers or to any coordinators. The notion of decentralized diagnosis is formalized by introducing the notion of codiagnosability that requires that a failure be detected by one of the diagnosers within a bounded delay. Algorithms of complexity polynomial in the size of the system and the nonfault specification are provided for: 1) testing codiagnosability, 2) computing the bound in delay of diagnosis, 3) offline synthesis of individual diagnosers, and 4) online diagnosis using them. The notion of codiagnosability and the above algorithms are initially presented in a setting of a specification language (violation of which represents a fault) and are later specialized to the case where faults are modeled as the occurrences of certain events. The notion of strong codiagnosability is also introduced to capture the ability of being certain about both the failure as well as the nonfailure conditions in a system within a bounded delay.