An Intelligent System by Fuzzy Reliability Algorithm in Fault Tree Analysis for Nuclear Power Plant Probabilistic Safety Assessment

Abstract

Fault tree analysis for nuclear power plant probabilistic safety assessment is an intricate process. Personal computer-based software systems have therefore been developed to conduct this analysis. However, all existing fault tree analysis software systems only accept quantitative data to characterized basic event reliabilities. In real-world applications, basic event reliabilities may not be represented by quantitative data but by qualitative justifications. The motivation of this work is to develop an intelligent system by fuzzy reliability algorithm in fault tree analysis, which can accept not only quantitative data but also qualitative information to characterized reliabilities of basic events. In this paper, a newly-developed system called InFaTAS-NuSA is presented and its main features and capabilities are discussed. To benchmark the applicability of the intelligent concept implemented in InFaTAS-NuSA, a case study is performed and the analysis results are compared to the results obtained from a well-known fault tree analysis software package. The results confirm that the intelligent concept implemented in InFaTAS-NuSA can be very useful to complement conventional fault tree analysis software systems.

A Fuzzy Probability Algorithm for Evaluating the AP1000 Long Term Cooling System to Mitigate Large Break LOCA

“…Two aspects of originalities of this study are (1) an introduction of fuzzy probabilities into fault tree analysis to describe the occurrence likelihoods of basic events, intermediate events and the top event; and (2) a substitution of Boolean algebra by fuzzy combination rules to quantify fault trees. This study is the further development of our previous studies in [15,19].…”

“…This number of experts has been properly implemeted in nuclear power plant safety assessment involving experts [15,19]. Those seven selected experts are then asked to qualitatively characterize the occurrence likelihoods of those 26 basic events.…”

A review of applications of fuzzy sets to safety and reliability engineering

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