Decentralized Prognosis of Failures in Discrete Event Systems

We study the prognosis of failures, i.e., their prediction prior to their occurrence, in discrete event systems in a decentralized setting where multiple prognosers use their local observations to issue local prognosis decisions. We define the notion of correctness of a decentralized set of prognosers in terms of "no missed detections" (each failure is prognosed prior to its occurrence) and "no false alarms" (an incorrect prognostic decision is never issued), and introduce the notion of coprognosability as an existence condition. When specialized to the centralized case (i.e., the case of a single prognoser), this condition turns out to be weaker than the one introduced by Genc and Lafortune in 2006 since a uniform bound on the number of steps within which a failure will occur is not required. For comparison, we also introduce the stronger notion of "uniformly bounded coprognosability" and identify the subclass of decentralized prognosers for which it serves as an existence condition. We show that the two notions coincide when the underlying system and its nonfailure specification possess finite-state representations, and present a verification algorithm whose complexity is polynomial in the sizes of the system being prognosed and its nonfailure specification, and is exponential in the number of the local prognosers. We also introduce the notion of reaction bound for coprognosis as the earliest time beyond a prognostic decision when a failure can occur, and present an algorithm for computing it. The complexity of this algorithm is identical to that of the verification algorithm. An algorithm with complexity linear in the size of the specification and the number of local prognosers is also presented for an online prognosis of failures. We show that the notions of coprognosability and its uniformly bounded version are in general incomparable with the notion of codiagnosability (that guarantees a uniformly bounded delay detection of a failure by a local diagnoser). When the system cannot execute an unbounded sequence of unobservable events, uniformly bounded coprognosability implies codiagnosability, whereas coprognosability and codiagnosability remain incomparable.