Diagnosis of a class of distributed discrete-event systems

Discrete-event modeling can be applied to a large variety of physical syste ms, such as digital hardware, quelling networks, communication networks, an d industrial protection systems, in order to support different tasks, inclu ding fault detection, monitoring, and diagnosis. This paper focuses on the model-based diagnosis of a class of distributed discrete-event systems, cal led active systems. An active system, which is designed to react to possibl y harmful external events, is modeled as a network of communicating automat a, where each automaton describes the behavior of a system component. Unlik e other approaches based on the synchronous composition of automata and on the off-line creation of the model of the entire system, the proposed diagn ostic technique deals with asynchronous events and does not need any global diagnoser to be built. Instead, the current approach features a problem-de composition/solution-composition nature whose core is the on-line progressi ve reconstruction of the behavior of the active system, guided by the avail able observations. This incremental technique makes effective the diagnosis of large-scale active systems, for which the one-shot generation of the gl obal model is almost invariably impossible in practice. The diagnostic meth od encompasses three steps: 1) reconstruction planning; 2) behavior reconst ruction; and 3) diagnosis generation, Step 1 draws a hierarchical decomposi tion of the behavior reconstruction problem. Reconstruction is made up in S tep 2, where an intensional representation of ail the dynamic behaviors whi ch are consistent with the available system observation is produced. Diagno sis is eventually generated in Step 3, based on the faulty evolutions incor porated within the reconstructed behaviors. The modular approach is formall y defined, with special emphasis on Steps 2 and 3, and applied to the power transmission network domain.