Diagnosability degree of stochastic discrete event systems

Bazille, Hugo; Fabre, Éric; Genest, Blaise

doi:10.1109/cdc.2017.8264524

Cited by 5 publications

(1 citation statement)

References 13 publications

(17 reference statements)

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“…• Firstly, in the framework of automaton, we will study the active diagnosis problem in the distributed [93][94][95][96] and probabilistic [97][98][99][100] settings. As far as we know, there is no work to deal with active diagnosis problem in the framework of distributed or probabilistic systems.…”

Section: Future Workmentioning

confidence: 99%

Design of Supervisors for Active Diagnosis in Discrete Event Systems

2020

IEEE Trans. Automat. Contr.

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In recent years, with the development of computer science and electronic information technology, many large and complex systems have emerged. When a system runs online, it is inevitable to produce some faults. If a fault in a system cannot be detected and treated in time, some serious accidents may occur, resulting in economic and human losses. Therefore, in both academia and industry, fault diagnosis has received considerable attention. A discrete event system is a discrete-state and event-driven system. In modern society, discrete event systems have wide practical application backgrounds. Many systems can be represented by discrete event systems, such as flexible manufacturing systems, smart urban transportation systems and computer communication networks. In recent years, the problem of fault diagnosis in discrete event systems has received much attention. The problem of fault diagnosis in discrete event systems is to determine if some faults have occurred by observing events generated by a plant. On the other hand, diagnosability is a property of a plant such that any fault can be detected within finite future steps after its occurrence. In practice, a plant should necessarily be diagnosable since any fault that has occurred must be detected and be repaired in time to guarantee its safety and reliability. This thesis considers two discrete event modeling formalisms and studies a problem of active diagnosis: it consists in modifying an undiagnosable plant's behavior by supervisory control, thus ensuring that the closed-loop system is diagnosable. The main results of this thesis are summarized as follows.

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