Complexity reduction techniques for quantified diagnosability of stochastic systems

Hugo Bazille 1 Eric Fabre 1 Blaise Genest 1
1 SUMO - SUpervision of large MOdular and distributed systems
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
Abstract : In a discrete event stochastic system, the natural notion of diagnosability, called A-diagnosability, requires that each fault event is eventually detected with probability one. Several definitions of diagnosability degree have been derived from this notion. They examine the detection probability after a fault occurs. To check diagnosability and compute diagnosability degrees, one usually attaches to the original stochastic system the information of a so-called diagnoser, which is in general exponentially larger than the original system. In this paper, we show that the full complexity of such diagnosers is not necessary, and that one can rely on simpler systems, with up to an exponential gain in complexity.
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Hugo Bazille, Eric Fabre, Blaise Genest. Complexity reduction techniques for quantified diagnosability of stochastic systems. WODES'18 - 14th IFAC Workshop on Discrete Event Systems, May 2018, Castellamare di Stabi, Italy. pp.82-87, ⟨10.1016/j.ifacol.2018.06.283⟩. ⟨hal-01943401⟩



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