Reliability analysis by mapping probabilistic importance factors into bayesian belief networks for making decision in water deluge system
Abstract
Liquid petroleum gas (LPG) is one area where catastrophic release scenarios have occurred. For this reason, preventive, and protective barriers have to be installed in order to reduce the occurrence and the severity of these scenarios. This article addresses an analysis of deluge system barrier and proposes a making decision process to ensure a high level of reliability, availability, maintainability, and safety (RAMS) using a robust Reliability Analysis with conditional probabilities. To achieve this RAMS target, a methodology for converting fault tree analysis (FTA) in continuous time using Monte Carlo (MC) simulation to Bayesian belief network (BBN) is developed. The probabilistic importance factors (PIFs) for critical components ranking and decision making are also mapped using BBN inferences in Water Deluge Systems (WDS) with an optimization aim using redundancy or maintenance tasks. This analysis illustrates the helpfulness of mapping PIFs into BBN for making a decision in any critical technological infrastructures.