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An optimized technique for reliability analysis of safety‐critical systems: A case study of nuclear power plant
Author(s) -
Kumar Pramod,
Singh Lalit Kumar,
Kumar Chiranjeev
Publication year - 2019
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2340
Subject(s) - markov chain , reliability engineering , reliability (semiconductor) , computer science , state space , life critical system , markov model , petri net , markov process , bipartite graph , stochastic petri net , nuclear power plant , event (particle physics) , system safety , graph , software , algorithm , engineering , power (physics) , mathematics , theoretical computer science , statistics , machine learning , physics , quantum mechanics , nuclear physics , programming language
Stochastic models are extensively used in quantifying the reliability of safety critical systems. These models use the state‐space model for reliability quantification. Markov chain is comprehensively used in describing a sequence of possible events of any system in which the probability of each event depends only on the state attained in the previous event. Markov chains are convenient to model the software system of the SCS with the help of Petri Nets, a directed bipartite graph widely used for the verification and validation of real‐time systems. However, the stochastic model suffers from the state‐space explosion problem. In this paper, we proposed a technique for reliability analysis of safety critical systems, excavating into the coherent optimization of Markov chain. The approach has been validated on 17 safety critical systems of nuclear power plants.