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Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure

Failure mode and effects analysis (FMEA) is an effective model that identifies the potential risk in the management process. In FMEA, the priority of the failure mode is determined by the risk priority number. There is enormous uncertainty and ambiguity in the traditional FMEA because of the diverge...

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Autores principales: Liu, Yiyi, Tang, Yongchuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042825/
https://www.ncbi.nlm.nih.gov/pubmed/35473954
http://dx.doi.org/10.1038/s41598-022-10828-2
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author Liu, Yiyi
Tang, Yongchuan
author_facet Liu, Yiyi
Tang, Yongchuan
author_sort Liu, Yiyi
collection PubMed
description Failure mode and effects analysis (FMEA) is an effective model that identifies the potential risk in the management process. In FMEA, the priority of the failure mode is determined by the risk priority number. There is enormous uncertainty and ambiguity in the traditional FMEA because of the divergence between expert assessments. To address the uncertainty of expert assessments, this work proposes an improved method based on the belief divergence measure. This method uses the belief divergence measure to calculate the average divergence of expert assessments, which is regarded as the reciprocal of the average support of assessments. Then convert the relative support among different experts into the relative weight of the experts. In this way, we will obtain a result with higher reliability. Finally, two practical cases are used to verify the feasibility and effectiveness of this method. The method can be used effectively in practical applications.
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spelling pubmed-90428252022-04-27 Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure Liu, Yiyi Tang, Yongchuan Sci Rep Article Failure mode and effects analysis (FMEA) is an effective model that identifies the potential risk in the management process. In FMEA, the priority of the failure mode is determined by the risk priority number. There is enormous uncertainty and ambiguity in the traditional FMEA because of the divergence between expert assessments. To address the uncertainty of expert assessments, this work proposes an improved method based on the belief divergence measure. This method uses the belief divergence measure to calculate the average divergence of expert assessments, which is regarded as the reciprocal of the average support of assessments. Then convert the relative support among different experts into the relative weight of the experts. In this way, we will obtain a result with higher reliability. Finally, two practical cases are used to verify the feasibility and effectiveness of this method. The method can be used effectively in practical applications. Nature Publishing Group UK 2022-04-26 /pmc/articles/PMC9042825/ /pubmed/35473954 http://dx.doi.org/10.1038/s41598-022-10828-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Yiyi
Tang, Yongchuan
Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure
title Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure
title_full Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure
title_fullStr Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure
title_full_unstemmed Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure
title_short Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure
title_sort managing uncertainty of expert’s assessment in fmea with the belief divergence measure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042825/
https://www.ncbi.nlm.nih.gov/pubmed/35473954
http://dx.doi.org/10.1038/s41598-022-10828-2
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