Decentralized Diagnosis of Stochastic Discrete Event Systems

Liu, Fuchun; Qiu, Daowen; Xing, Hongyan; Fan, Zhixiang

doi:10.1109/tac.2007.915172

Cited by 55 publications

(19 citation statements)

References 26 publications

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“…. , } \ { } ∧ ‖G‖ = do (9) sd = ConSSDia( , G) (10) if sd satisfies Theorem 17 then (11) return is A-diagnosable (12) end for (13) ++ (14) end while (15) return is not A-diagnosable (16) end for Algorithm 1: VerDia: verifying A-diagnosability of distributed SDES. Distributed SDESs exchange the local diagnosis information by the communication events.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…The probability of the transition is computed by (15). Suppose ∈ Ψ(Σ ) is the path ended with failure event in the global model.…”

Section: Theorem 12 If Is Locally A-diagnosable In a Local Model Thmentioning

confidence: 99%

“…SDES can present the system more precisely. In the context of testing diagnosability of SDES, a number of approaches have been proposed, including [11][12][13][14][15][16][17]. Reference [16] has defined safe diagnosability for SDES, in which failure detection occurs before any given forbidden string in the failed mode of system is executed.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, SDESs with decentralized information are separated into decentralized SDESs and distributed SDESs. A-diagnosability of decentralized SDESs has been presented in [15]. In order to improve the complexity of diagnosis in decentralized SDESs, [14] proposed a polynomial algorithm to check the A-diagnosability of decentralized SDESs.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Inspired by the DESs with decentralized information, SDESs with decentralized information are partitioned into decentralized SDESs and distributed SDESs. Failure diagnosis in decentralized SDESs was investigated in [14,15]. However, the previous literature only focused on the decentralized SDESs.…”

Section: Introductionmentioning

confidence: 99%

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Failure Diagnosis for Distributed Stochastic Discrete Event Systems

Geng

Ouyang

Zhao

2017

Mathematical Problems in Engineering

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Because of the complexity of the failure diagnosis for large-scale discrete event systems (DESs), DESs with decentralized information have received a lot of attention. DESs with communication events are defined as distributed DESs. Stochastic discrete event systems (SDESs) are DESs with a probabilistic structure. A-diagnosability is an important property in failure diagnosis of SDES. In this paper, we investigate A-diagnosability in distributed SDESs. We define a local model and global model. Moreover, we construct a synchronized stochastic diagnoser to check A-diagnosability in distributed SDESs. We also propose a necessary and sufficient condition for a distributed SDES to be A-diagnosable. Some examples are described to illustrate our algorithms.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…The probability of the transition is computed by (15). Suppose ∈ Ψ(Σ ) is the path ended with failure event in the global model.…”

Section: Theorem 12 If Is Locally A-diagnosable In a Local Model Thmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Failure Diagnosis for Distributed Stochastic Discrete Event Systems

Geng

Ouyang

Zhao

2017

Mathematical Problems in Engineering

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Estimation and Verification of Partially Observed Discrete‐Event Systems

Yin

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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State estimation is one of the most fundamental problems in the systems and control theory. In many real-world systems, it is not always possible to access the full information of the system due to measurement noises/uncertainties and the existence of adversaries. Therefore, how to estimate the state of the system is crucial when one wants to make decisions based on the limited and incomplete information.Given a system, one of the most important questions is to prove/disprove the correctness of the system with respect to some desired requirement (or specification). In particular, we would like to check the correctness of the system in a formal manner in the sense that the checking procedures are algorithmic and results have provable correctness guarantees. Such a formal satisfaction checking problem is referred to as the verification problem. Performing formal property verification is very important for many complicated but safety-critical infrastructures.This article considers state estimation and verification problems for an important class of man-made cyber-physical systems called Discrete-Event Systems (DES). Roughly speaking, DES are dynamic systems with discrete state-spaces and event-triggered dynamics. DES models are widely used in the study of complex automated systems where the behavior is inherently eventdriven, as well as in the study of discrete abstractions of continuous, hybrid, and/or cyber-physical systems. Over the past decades, the theory of DES has been successfully applied to many real-world problems, e.g., the control of

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Adaptive control and signal processing literature survey (No. 6)

2008

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please

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Decentralized Diagnosis of Stochastic Discrete Event Systems

Cited by 55 publications

References 26 publications

Failure Diagnosis for Distributed Stochastic Discrete Event Systems

Failure Diagnosis for Distributed Stochastic Discrete Event Systems

Estimation and Verification of Partially Observed Discrete‐Event Systems

Adaptive control and signal processing literature survey (No. 6)

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