scholarly journals Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiangfu Zhao ◽  
Gianfranco Lamperti ◽  
Dantong Ouyang ◽  
Xiangrong Tong
Keyword(s):  
Discrete Event Systems ◽  
Discrete Event ◽  
Sufficient Conditions ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Control Engineering ◽  
Event Systems ◽  
Active Research ◽  
Necessary And Sufficient ◽  
Static Systems

In the last several decades, the model-based diagnosis of discrete-event systems (DESs) has increasingly become an active research topic in both control engineering and artificial intelligence. However, in contrast with the widely applied minimal diagnosis of static systems, in most approaches to the diagnosis of DESs, all possible candidate diagnoses are computed, including nonminimal candidates, which may cause intractable complexity when the number of nonminimal diagnoses is very large. According to the principle of parsimony and the principle of joint-probability distribution, generally, the minimal diagnosis of DESs is preferable to a nonminimal diagnosis. To generate more likely diagnoses, the notion of the minimal diagnosis of DESs is presented, which is supported by a minimal diagnoser for the generation of minimal diagnoses. Moreover, to either strongly or weakly decide whether a minimal set of faulty events has definitely occurred or not, two notions of minimal diagnosability are proposed. Necessary and sufficient conditions for determining the minimal diagnosability of DESs are proven. The relationships between the two types of minimal diagnosability and the classical diagnosability are analysed in depth.

scholarly journals Failure Diagnosis for Distributed Stochastic Discrete Event Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xuena Geng ◽  
Dantong Ouyang ◽  
Xiangfu Zhao
Keyword(s):  
Large Scale ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Failure Diagnosis ◽  
Necessary And Sufficient Condition ◽  
Event Systems ◽  
Communication Events ◽  
Probabilistic Structure ◽  
Stochastic Discrete Event Systems ◽  
Necessary And Sufficient

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.

A Polynomial Algorithm for Diagnosability Analysis of Discrete Event Systems

2015 ◽  
Vol 6 (2) ◽  
pp. 1-22
Author(s):  
Eric Gascard ◽  
Zineb Simeu-Abazi ◽  
Bérangère Suiphon
Keyword(s):  
Discrete Event Systems ◽  
Discrete Event ◽  
Finite Sequence ◽  
Finite State Automaton ◽  
Bounded Number ◽  
Event Systems ◽  
Finite State ◽  
Definition Of ◽  
Necessary And Sufficient ◽  
Event Based

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.

Explanatory Diagnosis of Discrete-Event Systems with Temporal Information and Smart Knowledge-Compilation

2020 ◽  
Author(s):  
Nicola Bertoglio ◽  
Gianfranco Lamperti ◽  
Marina Zanella ◽  
Xiangfu Zhao
Keyword(s):  
Decision Making ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Knowledge Compilation ◽  
Regular Expressions ◽  
Combinational Circuits ◽  
Specific Diagnosis ◽  
Event Systems ◽  
State Changes ◽  
Static Systems

Model-based diagnosis is typically set-oriented. In static systems, such as combinational circuits, a candidate (or diagnosis) is a set of faulty components that explains a set of observations. In discrete-event systems (DESs), a candidate is a set of faulty events occurring in a sequence of state changes that conforms with a sequence of observations. Invariably, a candidate is a set. This set-oriented perspective makes diagnosis of DESs narrow in explainability, owing to the lack of any temporal knowledge relevant to the faults within a candidate, along with the inability to discriminate between single and multiple occurrences of the same fault. Embedding temporal knowledge in a candidate, such as the relative temporal ordering of faults and the multiplicity of the same fault, may be essential for critical decision making. To favor explainability, the notions of temporal fault, explanation, and explainer are introduced in diagnosis of DESs. The explanation engine reacts to a given sequence of observations by generating and refining in real-time a sequence of regular expressions, where the language of each expression is a set of temporal faults. Moreover, to avoid total knowledge compilation, the explainer can be generated incrementally either offline, based on meaningful behavioral scenarios, or online, when being operated in solving specific diagnosis problems.

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