Reliability Analysis of Relay Protection Based on the Fuzzy-Markov Model

Hao, Ganggang; Guan, Huanxin; Qiu, Dongxue; Du, Wei

doi:10.14257/ijhit.2015.8.10.11

Cited by 2 publications

(1 citation statement)

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“…Variants have been described in [63,107,147,174,218]. Fuzzy Markov model approaches have also been applied to a variety of systems, such as communication networks [173], power grids and smart meters [16,77,290], and degradable computing systems [170].…”

Section: Fig7 Fuzzy Markov Chain Examplementioning

confidence: 99%

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

Kabir

Papadopoulos

2018

International Journal of Approximate Reasoning

142

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Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering.

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