Abstract
Probabilistic risk assessment (PRA), sometimes called probabilistic safety analysis, quantifies the risk of undesired events in industrial facilities. However, one of the weaknesses that undermines the credibility and usefulness of this technique is the uncertainty in PRA results. Fault tree analysis (FTA) and event tree analysis (ETA) are the most important PRA techniques for evaluating system reliabilities and likelihoods of accident scenarios. Uncertainties, as incompleteness and imprecision, are present in probabilities of undesired events and failure rate data. Furthermore, both FTA and ETA traditionally assume that events are independent, assumptions that are often unrealistic and introduce uncertainties in data and modeling when using FTA and ETA. This work explores uncertainty handling approaches for analyzing the fault trees and event trees (method of moments) as a way to overcome the challenges of PRA. Applications of the developed frameworks and approaches are explored in illustrative examples, where the probability distributions of the top event of fault trees are obtained through the propagation of uncertainties of the failure probabilities of basic events. The application of the method of moments to propagate uncertainty of log-normal distributions showed good agreement with results available in the literature using different methods.
Highlights
Accidents at industrial facilities may result in serious consequences to workers, public, property, and the environment
Lack of knowledge about systems under study during the probabilistic risk assessment (PRA) is one of the main causes of uncertainties, which leads to simplification of assumptions, as well as imprecision and Reliability and Maintenance - An Overview of Cases inaccuracies in the parameters used as inputs to PRA
The main techniques used for probabilistic risk assessment are fault tree analysis (FTA) and event tree analysis (ETA) [11]
Summary
Accidents at industrial facilities may result in serious consequences to workers, public, property, and the environment. Fault tree analysis (FTA) and event tree analysis (ETA) are the most used techniques in PRAs. uncertainties in PRAs may lead to inaccurate risk level estimations and to wrong decisions [1]. A framework to use the method of moments for determining the likelihoods of different outcomes from event trees in an uncertain data environment using fault trees is described in this work. Illustrative examples using this approach for propagating uncertainty in basic events of fault trees, following log-normal distributions, are presented. The probability distributions of top events are compared with analyses available in the literature using different approaches, such as Monte Carlo simulation and Wilks and Fenton-Wilkinson methods
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