Abstract
Automata-theoretic models have been used successfully in model-based process supervision and diagnosis. From a practical viewpoint, their main drawback is their complexity, which increases fast with the size of the original discrete-event system. This complexity can be reduced by compositional modelling resulting in an automata network. The reduced complexity of the network leads to a complexity reduction of the diagnostic algorithm, as the fault diagnosis can be performed in a decentralised way. The paper develops such a diagnostic method for nondeterministic and stochastic automata networks.
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