Abstract
This paper proposes a global model of a set of alarm sequences that are generated by knowledge based system monitoring a dynamic process. The modelling approach is based on the Stochastic Approach to discover timed relations between discrete event classes from the representation of a set of sequences under the dual form of a homogeneous continuous time Markov chain and a superposition of Poisson processes. An abductive reasoning on these representations allows discovering chronicle models that can be used as diagnosis rules. Such rules subsume a temporal model called the average time sequence that sums up the initial set of sequences. This paper presents this model and the role it play in the analysis of an industrial process monitored with a network of industrial automata.
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