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New failure mode and effect analysis approach considering consensus under interval-valued intuitionistic fuzzy environment

  • Yan-Lai Li
  • Rui Wang
  • Kwai-Sang Chin
Methodologies and Application
  • 30 Downloads

Abstract

As a powerful pre-accident risk evaluation method, the traditional failure mode and effect analysis (FMEA) is extensively used to identify and eliminate the potential failure modes of products or processes, and presents several limitations simultaneously. To improve the accuracy of risk evaluation, this paper proposes a novel FMEA approach considering consensus level between decision makers. First, linguistic variables are applied to express the decision makers’ evaluation information of failure modes, which can be transformed into the corresponding interval-valued intuitionistic fuzzy (IVIF) numbers. Second, an IVIF consensus model is constructed to confirm whether the consensus is achieved, and subsequently, the collective evaluation matrix is aggregated by the interval-valued intuitionistic fuzzy prioritized weighted averaging operator. Third, a deviation maximization model is used to calculate the weights of risk factors. Finally, the improved IVIF-MULTIMOORA method is implemented to determine the risk ranking of failure modes. This paper also provides a numerical example to illustrate the validity and rationality of the proposed method.

Keywords

Failure mode and effect analysis Consensus model Interval-valued intuitionistic fuzzy set MULTIMOORA method Risk evaluation 

Notes

Acknowledgements

This study was funded by the National Natural Science Foundation of China (Nos. 71371156, 70971017) and Doctoral Innovation Fund Program of Southwest Jiaotong University (No. D-CX201727).

Compliance with ethical standards

Conflict of Interest

The authors declare no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Transportation and LogisticsSouthwest Jiaotong UniversityChengduPeople’s Republic of China
  2. 2.National Lab of Railway TransportationSouthwest Jiaotong UniversityChengduPeople’s Republic of China
  3. 3.Department of System Engineering and Engineering ManagementCity University of Hong KongKowloon TongPeople’s Republic of China

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