Robust diagnosis of discrete event systems against intermittent loss of observations

In the usual approaches to fault diagnosis of discrete event systems it is assumed that not only all sensors work properly but also all information reported by sensors always reaches the diagnoser. Any bad sensor operation or communication failure between sensors and the diagnoser can be regarded as loss of observations of events initially assumed as observable. In such situations, it may be possible that either the diagnoser stands still or report some wrong information regarding the fault occurrence. In this paper we assume that intermittent loss of observations may occur and we propose an automaton model based on a new language operation (language dilation) that takes it into account. We refer to this problem as robust diagnosability against intermittent loss of observations (or simply robust diagnosability, where the context allows). We present a necessary and sufficient condition for robust diagnosability in terms of the language generated by the original automaton and propose two tests for robust language diagnosability, one that deploys diagnosers and another one that uses verifiers. We also extend the results to robust codiagnosability against intermittent loss of observations.