Quantification of Temporal Fault Trees Based on Fuzzy Set Theory

Kabir, Sohag; Edifor, Ernest; Walker, Martin; Gordon, Neil

doi:10.1007/978-3-319-07013-1_24

Cited by 10 publications

(11 citation statements)

References 15 publications

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“…theory, membership can take any value between 0 and 1 that reflects certain degrees of membership and there is no practical limit on the choice of fuzzy membership values (Hansen, 2005;Lee, 2007;Kabir et al, 2014;Ghosh et al, 2012). The Fuzzy Logic method creates more flexible compositions of weighted maps and can be easily implemented with GIS modeling language (Lee, 2007).…”

Section: Methodsmentioning

confidence: 99%

Surface Water Contamination Risk Assessment Modeled by Fuzzy‐WRASTIC

Alavipoor¹,

Ghorbaninia²,

Karimi³

et al. 2016

Water Environment Research

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This research provides a Fuzzy-WRASTIC new model for water resource contamination risk assessment in a GIS (Geographic Information System) environment. First, this method setting in a multi-criteria evaluation framework (MCE) reviewed and mapped the sub criteria of every above-mentioned criterion. Then, related sub-layers were phased by the observance of GIS environment standards. In the next step, first the sub-layers were combined together, next the modeling of pollution risk status was done by utilizing a fuzzy overlay method and applying the OR, AND, SUM, PRODUCT and GAMMA operators by using WLC (Weighted Linear Combination) method and providing weights in the WRASTIC model. The results provide the best combination of modeling and the percentages of its risk categories of low, medium, high and very high, which are respectively 1.8, 14.07, 51.43 and 32.7. More areas have severe risk due to the unbalanced arrangement and compact of land uses around the compact surface water resources.

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Section: Methodsmentioning

confidence: 99%

Surface Water Contamination Risk Assessment Modeled by Fuzzy‐WRASTIC

Alavipoor¹,

Ghorbaninia²,

Karimi³

et al. 2016

Water Environment Research

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“…(Li, Huang, Liu, Xiao, & Li, 2012;Li, Mi, Liu, Yang, & Huang, 2015;Verma, Srividya, Prabhudeva, & Vinod, 2006;Yang, 2011)) and temporal fault trees (e.g. (Kabir, Edifor, Walker, & Gordon, 2014;Kabir, Walker, Papadopoulos, Rüde, & Securius, 2016)).…”

Section: Fuzzy Fault Treesmentioning

confidence: 99%

An overview of fault tree analysis and its application in model based dependability analysis

Kabir

2017

Expert Systems with Applications

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335

134

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Fault Tree Analysis (FTA) is a well-established and well-understood technique, widely used for dependability evaluation of a wide range of systems. Although many extensions of fault trees have been proposed, they suffer from a variety of shortcomings. In particular, even where software tool support exists, these analyses require a lot of manual effort. Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of modelbased dependability analysis (MBDA). Different tools and techniques have been developed as part of MBDA to automate the generation of dependability analysis artefacts such as fault trees. Firstly, this paper reviews the standard fault tree with its limitations. Secondly, different extensions of standard fault trees are reviewed. Thirdly, this paper reviews a number of prominent MBDA techniques where fault trees are used as a means for system dependability analysis and provides an insight into their working mechanism, applicability, strengths and challenges. Finally, the future outlook for MBDA is outlined, which includes the prospect of developing expert and intelligent systems for dependability analysis of complex open systems under the conditions of uncertainty.

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“…where P(TE) is the TE's occurrence probability. To address the uncertainty associated with the failure data and system behaviour, fuzzy set theory has been used by several researchers such as (Halloul et al, 2019;Shi et al, 2014;Tanaka et al, 1983;Kabir et al, 2016;Yuhua and Datao, 2005;Markowski et al, 2009;Kabir et al, 2014a). It is worthwhile noting that, FT software often use"rare event approximation" i.e.…”

Section: Fault Tree Analysismentioning

confidence: 99%

Dynamic reliability assessment of flare systems by combining fault tree analysis and Bayesian networks

Kabir

Taleb‐Berrouane

Papadopoulos

2019

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Flaring is a combustion process commonly used in the oil and gas industry to dispose flammable waste gases. Flare flameout occurs when these gases escape unburnt from the flare tip causing the discharge of flammable and/or toxic vapor clouds. The toxic gases released during this process have the potential to initiate safety hazards and cause serious harm to the ecosystem and human health. Flare flameout could be caused by environmental conditions, equipment failure and human error. However, to better understand the causes of flare flameout, a rigorous analysis of the behaviour of flare systems under failure conditions is required. In this article, we used fault tree analysis (FTA) and the dynamic Bayesian network (DBN) to assess the reliability of flare systems. In this study, we analysed 40 different combinations of basic events that can cause flare flameout to determine the event with the highest impact on system failure. In the quantitative analysis, we use both constant and time-dependent failure rates of system components. The results show that combining these two approaches allows for robust probabilistic reasoning on flare system reliability, which can help improving the safety and asset integrity of process facilities. The proposed DBN model constitutes a significant step to improve the safety and reliability of flare systems in the oil and gas industry.

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Quantification of Temporal Fault Trees Based on Fuzzy Set Theory

Cited by 10 publications

References 15 publications

Surface Water Contamination Risk Assessment Modeled by Fuzzy‐WRASTIC

Surface Water Contamination Risk Assessment Modeled by Fuzzy‐WRASTIC

An overview of fault tree analysis and its application in model based dependability analysis

Dynamic reliability assessment of flare systems by combining fault tree analysis and Bayesian networks

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