Diagnosis of large active systems

P. Baroni, G. Lamperti, P. Pogliano, M. Zanella

Artificial Intelligence, 110 (1), 1999, 135-183



This paper presents a modular technique, amenable to parallel implementation, for the diagnosis of large-scale, distributed, asynchronous event-driven (namely, active) systems. An active system is an abstraction of a physical system that can be modeled as a network of communicating automata. Due to the distributed nature of the class of systems considered, and unlike other approaches based on synchronous composition of automata, exchanged events are buffered within communication links and dealt with asynchronously. The main goal of the diagnostic technique is the reconstruction of the behavior of the active system starting from a set of observable events. The diagnostic process involves three steps: interpretation, merging, and diagnosis generation. Interpretation generates a representation of the behavior of a part of the active system based on observable events. Merging combines the result of several interpretations into a new, broader interpretation. The eventual diagnostic information is generated on the basis of fault events possibly incorporated within the reconstructed behavior. In contrast with other approaches, the proposed technique does not require the generation of the, possibly huge, model of the entire system, typically, in order to yield a global diagnoser, but rather, it allows a modular and parallel exploitation of the reconstruction process. This property, to a large extent, makes effective the diagnosis of real active systems, for which the reconstruction of the global behavior is often unnecessary, if not impossible.

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