An intelligent system by fuzzy reliability algorithm in fault tree analysis for nuclear power plant probabilistic safety assessment

© Imperial College Press. 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

Models for the Reliability Analysis of Digital Instrumentation and Control Systems for Nuclear Power Plants

The objective of this chapter is to discuss two approaches for reliability analysis of digital instrumentation and control systems in nuclear power plants taking into account the regulatory side. Dynamic Flowgraph Methodology (DFM) and Markov/Cell-to-Cell Mapping Technique (CCMT) are discussed and case studies developed are presented. These case studies involve simplified control systems for a steam generator and a pressurizer of a Pressurized Water Reactor (PWR) plant for the purpose of evaluating each method. Advantages and limitations of each approach are addressed. For the DFM approach, three concerns in the literature are addressed: modeling of the system itself, incorporation of the methodology results into existing Probabilistic Safety Assessments (PSA), and identification of software failures. The Markov/CCMT, which has been used in dynamic probabilistic safety assessments, is approached by means of a simplified digitally controlled water volume control system. The Markov/CCMT methodology results in detailed data of the system reliability behavior in relation to time. However, it demands a higher computational effort than usual as the complexity (i.e., number of components and failure states) of the system increases. As a regulatory research conclusion, the methodologies presented can be used on PSA risk informed assessment, contributing to the regulatory side