Robust diagnosis of discrete-event systems against permanent loss of observations

We consider the problem of diagnosing the occurrence of a certain unobservable event of interest, the fault event, in the operation of a partially-observed discrete-event system subject to permanent loss of observations modeled by a finite-state automaton. Specifically, it is assumed that certain sensors for events that would a priori be observable may fail at the outset, thereby resulting in a loss of observable events; the diagnostic engine is not directly aware of such sensor failures. We explore a previous definition of robust diagnosability of a given fault event despite the possibility of permanent (and unknown a priori) loss of observations and present a polynomial time verification algorithm to verify robust diagnosability and a methodology to perform online diagnosis in this scenario using a set of partial diagnosers.