Complexity reduction techniques for quantified diagnosability of stochastic systems

International audienceIn 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