Sensitivity and importance analysis of Markov models using perturbation analysis: application in reliability studies

Sensitivity (or importance analysis) has been first defined for “static systems”, i.e. systems described by combinatorial reliability models (fault or event trees) and several measures, both structural and probabilistic, have been proposed to assess component importance. For dynamic systems including inter-component and functional dependencies (cold spare, shared load, shared ressources, ....), and described by Markov models or, more generally, by discrete events dynamic systems models (DEDS), the problem of sensitivity analysis remains widely open. In this paper we propose to use the estimation method developed by Cao in (Cao & Chen 1997) in the framework of Perturbation Analysis, to formalize several sensitivity measures in case of dynamic systems. We show with numerical examples why this method offers a promising tool for steady state sensitivity analysis of Markov Processes in reliability studies.