Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yongchuan Tang, Zhaoxing Sun, Deyun Zhou, Yubo Huang
{"title":"Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method","authors":"Yongchuan Tang, Zhaoxing Sun, Deyun Zhou, Yubo Huang","doi":"10.1007/s40747-023-01268-0","DOIUrl":null,"url":null,"abstract":"<p>Failure mode and effects analysis (FMEA) is an important risk analysis tool that has been widely used in diverse areas to manage risk factors. However, how to manage the uncertainty in FMEA assessments is still an open issue. In this paper, a novel FMEA model based on the improved pignistic probability transformation function in Dempster–Shafer evidence theory (DST) and grey relational projection method (GRPM) is proposed to improve the accuracy and reliability in risk analysis with FMEA. The basic probability assignment (BPA) function in DST is used to model the assessments of experts with respect to each risk factor. Dempster’s rule of combination is adopted for fusion of assessment information from different experts. The improved pignistic probability function is proposed and used to transform the fusion result of BPA into probability function for getting more accurate decision-making result in risk analysis with FMEA. GRPM is adopted to determine the risk priority order of all the failure modes to overcome the shortcoming in traditional risk priority number in FMEA. Applications in aircraft turbine rotor blades and steel production process are presented to show the rationality and generality of the proposed method.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"4 2","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complex & Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s40747-023-01268-0","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Failure mode and effects analysis (FMEA) is an important risk analysis tool that has been widely used in diverse areas to manage risk factors. However, how to manage the uncertainty in FMEA assessments is still an open issue. In this paper, a novel FMEA model based on the improved pignistic probability transformation function in Dempster–Shafer evidence theory (DST) and grey relational projection method (GRPM) is proposed to improve the accuracy and reliability in risk analysis with FMEA. The basic probability assignment (BPA) function in DST is used to model the assessments of experts with respect to each risk factor. Dempster’s rule of combination is adopted for fusion of assessment information from different experts. The improved pignistic probability function is proposed and used to transform the fusion result of BPA into probability function for getting more accurate decision-making result in risk analysis with FMEA. GRPM is adopted to determine the risk priority order of all the failure modes to overcome the shortcoming in traditional risk priority number in FMEA. Applications in aircraft turbine rotor blades and steel production process are presented to show the rationality and generality of the proposed method.

基于改进的概率变换函数和灰色关联投影法的失效模式和影响分析
失效模式与影响分析(FMEA)是一种重要的风险分析工具,已广泛应用于不同领域,用于管理风险因素。然而,如何管理FMEA评估中的不确定性仍然是一个悬而未决的问题。为了提高FMEA风险分析的准确性和可靠性,本文提出了一种新的FMEA模型,该模型基于Dempster–Shafer证据理论(DST)中改进的概率变换函数和灰色关系投影法(GRPM)。DST中的基本概率分配(BPA)函数用于对专家对每个风险因素的评估进行建模。采用Dempster组合规则对不同专家的评估信息进行融合。提出了改进的概率函数,并将BPA的融合结果转化为概率函数,以获得更准确的FMEA风险分析决策结果。采用GRPM来确定所有故障模式的风险优先级,以克服FMEA中传统风险优先级数的不足。介绍了该方法在航空涡轮转子叶片和钢铁生产过程中的应用,证明了该方法的合理性和通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信