Diagnosis of Temporal Faults in Discrete-Event Systems

Model-based diagnosis of discrete-event systems (DESs) generates a set of candidates upon the reception of a temporal observation. In the literature, a candidate is a set of faults produced by a trajectory of the DES that is consistent with the temporal observation. As such, a candidate does not convey any temporal relationship between faults, nor does it account for multiple occurrences of the same fault. To overcome the limitations of this set-oriented approach to diagnosis of DESs, the novel notions of temporal fault and temporal diagnosis are proposed, along with two diagnosis techniques. A temporal fault is the (possibly unbounded) sequence of faults produced by a trajectory. A temporal diagnosis is a (possibly infinite) set of temporal faults. Hence, in this new temporal-oriented approach to diagnosis of DESs, a candidate is a temporal fault. The fact that a temporal diagnosis turns out to be a regular language is key to coping with the infinity of candidates, which can be represented by a regular expression. The diagnosis task can be performed either by restricting the DES space to the trajectories that are consistent with the temporal observation, or by exploiting a temporal diagnoser which allows for fast online diagnosis. The claim of this paper is that the extra temporal information embedded in candidates may be essential in taking critical decisions based on the diagnosis results.