Paper Info
Title
Improved failure mode and effect analysis with interval-valued intuitionistic fuzzy rough number theory
Abstract
Failure mode and effect analysis (FMEA) is a prospective risk analysis instrument applied in various fields for eliminating known or potential failures in products. However, conventional FMEA has suffered from plenty of criticisms that limit its effectiveness. Due to fuzzy and imprecise information often exist in risk assessments, numerous modified FMEA methods based on fuzzy theory are employed to improve the classical FMEA. However, the previous fuzzy-based FMEA approaches still have some drawbacks, e.g., requisition of extra information, no ability in handling hesitation of individual and subjectivity of group simultaneously, consideration of only three risk elements, and deeming the experts’ judgments are rational among most of them. Hence, this paper develops a novel concept of interval-valued intuitionistic fuzzy rough number (IVIFRN) by utilizing the merit of interval-valued intuitionistic fuzzy set in handling fuzziness and hesitation of individuals’ judgments and the benefit of rough number in manipulating imprecision and subjectivity of group’s assessments and presents a new FMEA model based on IVIFRN. Then, maintenance is regarded as a new risk aspect and to further establish a risk evaluation structure containing eight risk elements. Moreover, the experts’ bounded rationality and risk elements’ importance are determined by two synthetic weighting methods considering both subjective and objective information. Finally, a real case is performed to illustrate the effectiveness of the developed model.
Year
DOI
Venue
2020
10.1016/j.engappai.2020.103856
Engineering Applications of Artificial Intelligence
Keywords
DocType
Volume
Failure mode and effect analysis,Risk management,Interval-valued intuitionistic fuzzy set,Rough number,Hierarchical TOPSIS
Journal
95
ISSN
Citations 
PageRank 
0952-1976
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Guangquan Huang134.09
Liming Xiao233.07
Genbao Zhang3810.98