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An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy

Failure Mode and Effects Analysis (FMEA) has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications. However, how to determine the weights of team members appropriately, with the impact factor of domain experts’ uncertainty in decision-m...

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Detalles Bibliográficos
Autores principales: Liu, Zeyi, Xiao, Fuyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514692/
https://www.ncbi.nlm.nih.gov/pubmed/33266926
http://dx.doi.org/10.3390/e21020211
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author Liu, Zeyi
Xiao, Fuyuan
author_facet Liu, Zeyi
Xiao, Fuyuan
author_sort Liu, Zeyi
collection PubMed
description Failure Mode and Effects Analysis (FMEA) has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications. However, how to determine the weights of team members appropriately, with the impact factor of domain experts’ uncertainty in decision-making of FMEA, is still an open issue. In this paper, a new method to determine the weights of team members, which combines evidence theory, intuitionistic fuzzy sets (IFSs) and belief entropy, is proposed to analyze the failure modes. One of the advantages of the presented model is that the uncertainty of experts in the decision-making process is taken into consideration. The proposed method is data driven with objective and reasonable properties, which considers the risk of weights more completely. A numerical example is shown to illustrate the feasibility and availability of the proposed method.
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spelling pubmed-75146922020-11-09 An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy Liu, Zeyi Xiao, Fuyuan Entropy (Basel) Article Failure Mode and Effects Analysis (FMEA) has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications. However, how to determine the weights of team members appropriately, with the impact factor of domain experts’ uncertainty in decision-making of FMEA, is still an open issue. In this paper, a new method to determine the weights of team members, which combines evidence theory, intuitionistic fuzzy sets (IFSs) and belief entropy, is proposed to analyze the failure modes. One of the advantages of the presented model is that the uncertainty of experts in the decision-making process is taken into consideration. The proposed method is data driven with objective and reasonable properties, which considers the risk of weights more completely. A numerical example is shown to illustrate the feasibility and availability of the proposed method. MDPI 2019-02-22 /pmc/articles/PMC7514692/ /pubmed/33266926 http://dx.doi.org/10.3390/e21020211 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Zeyi
Xiao, Fuyuan
An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy
title An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy
title_full An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy
title_fullStr An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy
title_full_unstemmed An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy
title_short An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy
title_sort intuitionistic evidential method for weight determination in fmea based on belief entropy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514692/
https://www.ncbi.nlm.nih.gov/pubmed/33266926
http://dx.doi.org/10.3390/e21020211
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