Flexible diagnosis of discrete-event systems by similarity-based reasoning techniques

Diagnosis of discrete-event systems (DESs) may be improved by knowledge-compilation techniques, where a large amount of model-based reasoning is anticipated off-line, by simulating the behavior of the system and generating suitable data structures (compiled knowledge) embedding diagnostic information. This knowledge is exploited on-line, based on the observation of the system behavior, so as to generate the set of candidate diagnoses (problem solution). This paper makes a step forward: the solution of a diagnostic problem is supported by the solution of another problem, provided the two problems are somewhat similar. Reuse of model-based reasoning is thus achieved by exploiting the diagnostic knowledge yielded for solving previous problems. The technique still works when the available knowledge does not fit the extent of the system, but only a partition of it, that is, when solutions are available for subsystems only. In this case, the fragmented knowledge is exploited in a modular way, where redundant computation is avoided. Similarity-based diagnosis is meant for large-scale DESs, where the degree of similarity among subsystems is high and stringent time constraints on the diagnosis response is a first-class requirement.