Fuzzy Bayesian system reliability assessment based on Pascal distribution

Gholizadeh, Ramin; Shirazi, Aliakbar Mastani; Gildeh, Bahram Sadeghpour; Deiri, Einolah

doi:10.1007/s00158-009-0396-y

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…Different approaches consider different system parameters as fuzzy. For instance, in [64,86,215,234,245,246], the failure data is fuzzy, while in Li et al [122] quantify human reliability using triangular fuzzy numbers. Simon and Weber [208] considered system states as fuzzy, while Yanfu and Min [255] integrated fault trees with Bayesian networks, i.e., they translated fuzzy fault trees into BNs for reliability evaluation.…”

Section: Fuzzy Bayesian Network Approachesmentioning

confidence: 99%

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

Kabir

Papadopoulos

2018

International Journal of Approximate Reasoning

149

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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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Section: Fuzzy Bayesian Network Approachesmentioning

confidence: 99%

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

Kabir

Papadopoulos

2018

International Journal of Approximate Reasoning

149

View full text Add to dashboard Cite

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“…In the area of reliability prediction, fuzzy logic is a complicated approach, but offers major advantages regarding reliability assessment. Due to the lack of precision of the operating data in industrial equipment provided to the user who studied reliability assessment problems using expert systems, the fuzzy expert system used arranged mechanisms to deal with uncertain information [23, 24,25,26,27,28]. In this work, the views of several experts can be reconciled within a model of the systems based on fuzzy rules by using comparison techniques to provide reliable decisions.…”

Section: Reliability Fuzzy Expert Systemmentioning

confidence: 99%

Reliability Evaluation Based on a Fuzzy Expert System: Centrifugal Pump Application

Rachid¹,

Hafaifa²,

Hadroug³

et al. 2016

SIC

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In the development process of modern industrial systems, predictive reliability becomes very important in the design and exploitation of industrial facilities. This paper proposes the development of a new approach to the assessment of predictive reliability, applied to a centrifugal pump based on a fuzzy expert system to improve its performance under actual operating conditions. The obtained results clearly demonstrate how the reproduction of the main dynamic characteristics of the examined pump, using the proposed fuzzy model enables better performance regarding reliability.

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“…Huang, Lin, and Ke (2008) discussed a two-unit repairable systems suffering common-cause failure where the time to failure follows fuzzified exponential distribution. Gholizadeh et al (2010) applied the Bayesian approach to fuzzy parameters and created the fuzzy Bayes point estimator of system by 'Resolution Identity' in fuzzy set theory based on Pascal distribution. Finally, Taheri andZarei (2011) employed Wu's approach (2004) to construct the fuzzy Bayes estimator of system reliability for series system and parallel system from the conventional Bayes estimators by applying a 'Resolution Identity' for fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

System reliability for non-repairable multi-state series–parallel system using fuzzy Bayesian inference based on prior interval probabilities

Bamrungsetthapong

Pongpullponsak

2014

International Journal of General Systems

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In this article, the fuzzy concepts are applied in analysis of the system reliability problem. The fuzzy number is used to construct the fuzzy reliability of the non-repairable multi-state series-parallel system (NMSS). The fuzzy failure rate function is represented by an exponential fuzzy number. By using this innovative approach, the fuzzy system reliability of NMSS is created. In order to analyse this fuzzy system reliability, the fuzzy Bayesian point estimate of fuzzy system reliability is made by the conventional Bayesian formula. And, the posterior fuzzy system reliability of NMSS is developed by Bayesian inference with fuzzy probabilities. Finally, the performance of the method is measured by the mean square error of fuzzy Bayesian point estimate for the fuzzy system reliability of NMSS.

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Fuzzy Bayesian system reliability assessment based on Pascal distribution

Cited by 11 publications

References 14 publications

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

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

Reliability Evaluation Based on a Fuzzy Expert System: Centrifugal Pump Application

System reliability for non-repairable multi-state series–parallel system using fuzzy Bayesian inference based on prior interval probabilities

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