Discovering operational signatures with time constraints from a discrete event sequence

This paper aims at showing a method to discover signatures (or models of chronicles) from a discrete event sequence (alarms) generated by a monitoring cognitive agent (MCA). When the counting process of the events generated by a couple (process, MCA) behaves like a Poisson process, this couple can be considered as stochastic discrete event generator SDEG_{(pr, MCA)} and modeled as a superposition of Poisson and an homogeneous discrete time Markov chain. The ´BJT´ algorithm uses these two representations in order to help in the discovering of signatures. The results obtained on an industrial process monitored with a Sachem system have been validated by experts, confirming so the relevance of the approach within an industrial frame.