K-diagnosability analysis of bounded and unbounded Petri nets using linear optimization

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.

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K-diagnosability analysis of bounded and unbounded Petri nets using linear optimization

Cited by 3 publications

References 30 publications

Fault-prognosability, K-step prognosis and K-step predictive diagnosis in partially observed petri nets by means of algebraic techniques

Fault-prognosability, K-step prognosis and K-step predictive diagnosis in partially observed petri nets by means of algebraic techniques

An Efficient Algorithm for K-Diagnosability Analysis of Bounded and Unbounded Petri Nets

Silent closure based pair verifier for fault pattern diagnosis of discrete event systems

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