Cargando…
Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis
Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S), and detection (D) of a failure mode. To deal...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516733/ https://www.ncbi.nlm.nih.gov/pubmed/33286052 http://dx.doi.org/10.3390/e22030280 |
_version_ | 1783587069371613184 |
---|---|
author | Zheng, Haixia Tang, Yongchuan |
author_facet | Zheng, Haixia Tang, Yongchuan |
author_sort | Zheng, Haixia |
collection | PubMed |
description | Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S), and detection (D) of a failure mode. To deal with the uncertainty in the assessments given by domain experts, a novel Deng entropy weighted risk priority number (DEWRPN) for FMEA is proposed in the framework of Dempster–Shafer evidence theory (DST). DEWRPN takes into consideration the relative importance in both risk factors and FMEA experts. The uncertain degree of objective assessments coming from experts are measured by the Deng entropy. An expert’s weight is comprised of the three risk factors’ weights obtained independently from expert’s assessments. In DEWRPN, the strategy of assigning weight for each expert is flexible and compatible to the real decision-making situation. The entropy-based relative weight symbolizes the relative importance. In detail, the higher the uncertain degree of a risk factor from an expert is, the lower the weight of the corresponding risk factor will be and vice versa. We utilize Deng entropy to construct the exponential weight of each risk factor as well as an expert’s relative importance on an FMEA item in a state-of-the-art way. A case study is adopted to verify the practicability and effectiveness of the proposed model. |
format | Online Article Text |
id | pubmed-7516733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75167332020-11-09 Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis Zheng, Haixia Tang, Yongchuan Entropy (Basel) Article Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S), and detection (D) of a failure mode. To deal with the uncertainty in the assessments given by domain experts, a novel Deng entropy weighted risk priority number (DEWRPN) for FMEA is proposed in the framework of Dempster–Shafer evidence theory (DST). DEWRPN takes into consideration the relative importance in both risk factors and FMEA experts. The uncertain degree of objective assessments coming from experts are measured by the Deng entropy. An expert’s weight is comprised of the three risk factors’ weights obtained independently from expert’s assessments. In DEWRPN, the strategy of assigning weight for each expert is flexible and compatible to the real decision-making situation. The entropy-based relative weight symbolizes the relative importance. In detail, the higher the uncertain degree of a risk factor from an expert is, the lower the weight of the corresponding risk factor will be and vice versa. We utilize Deng entropy to construct the exponential weight of each risk factor as well as an expert’s relative importance on an FMEA item in a state-of-the-art way. A case study is adopted to verify the practicability and effectiveness of the proposed model. MDPI 2020-02-28 /pmc/articles/PMC7516733/ /pubmed/33286052 http://dx.doi.org/10.3390/e22030280 Text en © 2020 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 Zheng, Haixia Tang, Yongchuan Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis |
title | Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis |
title_full | Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis |
title_fullStr | Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis |
title_full_unstemmed | Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis |
title_short | Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis |
title_sort | deng entropy weighted risk priority number model for failure mode and effects analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516733/ https://www.ncbi.nlm.nih.gov/pubmed/33286052 http://dx.doi.org/10.3390/e22030280 |
work_keys_str_mv | AT zhenghaixia dengentropyweightedriskprioritynumbermodelforfailuremodeandeffectsanalysis AT tangyongchuan dengentropyweightedriskprioritynumbermodelforfailuremodeandeffectsanalysis |