Qualitative analysis of a BDMP by finite automata

The Boolean Driven Markov Processes (BDMPs) were developed by EDF to conduct predictive risk modelling and assessment on critical systems. A BDMP model is a description of the combinations of failures that makes a system to fail like it is done with Fault tree models. The calculation of all the sequences of events that conduct to the global failure is complex as they are implicitly represented by a BDMP that generates a large state space. This paper provides a semantic analysis of BDMP models conducted within the languages and finite automaton theories in order to develop a systematic way to obtain an extensive representation of all scenarios contained in a BDMP. This representation is obtained by using an algorithm given in this paper which constructs an equivalent finite automaton of a BDMP. This FA representation leads to the possibility of extracting all the minimal scenarios of failure implicitly described by a BDMP.

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