Homayoon Dezfuli

Developing Probabilistic Safety Performance Margins for Unknown and Underappreciated Risks

Probabilistic safety requirements currently formulated or proposed for space systems, nuclear reactor systems, nuclear weapon systems, and other types of systems that have a low-probability potential for high-consequence accidents depend on showing that the probability of such accidents is below a specified safety threshold or goal. Verification of compliance depends heavily upon synthetic modeling techniques such as PRA. To determine whether or not a system meets its probabilistic requirements, it is necessary to consider whether there are significant risks that are not fully considered in the PRA either because they are not known at the time or because their importance is not fully understood. The ultimate objective is to establish a reasonable margin to account for the difference between known risks and actual risks in attempting to validate compliance with a probabilistic safety threshold or goal. In this paper, we examine data accumulated over the past 60 years primarily from the space program, and secondarily from nuclear reactor experience, aircraft systems, and human reliability experience to formulate guidelines for estimating probabilistic margins to account for risks that are initially unknown or underappreciated. The formulation includes a review of the safety literature to identify the principal causes of such risks.

Probability-informed testing for reliability assurance through Bayesian hypothesis methods

Bayesian inference techniques play a central role in modern risk and reliability evaluations of complex engineering systems. These techniques allow the system performance data and any relevant associated information to be used collectively to calculate the probabilities of various types of hypotheses that are formulated as part of reliability assurance activities. This paper proposes a methodology based on Bayesian hypothesis testing to determine the number of tests that would be required to demonstrate that a system-level reliability target is met with a specified probability level. Recognizing that full-scale testing of a complex system is often not practical, testing schemes are developed at the subsystem level to achieve the overall system reliability target. The approach uses network modeling techniques to transform the topology of the system into logic structures consisting of series and parallel subsystems. The paper addresses the consideration of cost in devising subsystem level test schemes. The developed techniques are demonstrated using several examples. All analyses are carried out using the Bayesian analysis tool WinBUGS, which uses Markov chain Monte Carlo simulation methods to carry out inference over the network.