The reliability analysis for dynamic systems is often based on timed stochastic
Petri net models. The contribution of this paper concerns the design and
identification of non-Markovian timed stochastic Petri net models. In
particular, stochastic Petri nets that combine firing periods with normal and
exponential probability density functions (pdfs) are considered. A numerical
method is proposed for the identification of the parameters that characterize
the pdfs of the firing periods. This method is based on a statistical analysis
of the collected event sequences that are recorded by supervision systems. The
method is applied for the reliability analysis of a single server with variable
operation periods.
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