A New Model for Failure Mode and Effect Analysis Integrating Linguistic Z-Numbers and Projection Method

As a proactive reliability analysis tool, failure mode and effect analysis (FMEA) was widely utilized in various industries to guarantee safety and reliability. Recently, many researchers in this field have emphasized the limitations of this technique, such as in failure mode evaluation, risk factor...

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Veröffentlicht in:IEEE transactions on fuzzy systems Jg. 29; H. 3; S. 530 - 538
Hauptverfasser: Huang, Jia, Xu, Dong-Hui, Liu, Hu-Chen, Song, Ming-Shun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:1063-6706, 1941-0034
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Zusammenfassung:As a proactive reliability analysis tool, failure mode and effect analysis (FMEA) was widely utilized in various industries to guarantee safety and reliability. Recently, many researchers in this field have emphasized the limitations of this technique, such as in failure mode evaluation, risk factor weighting, and failure mode prioritization. The objective of this article is to develop a new FMEA model combining linguistic Z-numbers and an extended projection method to enhance the inherent characteristics of FMEA. Specifically, the linguistic Z-numbers are used to express experts' risk assessment information and the reliability of the assessment result. The normal projection method is extended to determine the risk priority of the failure modes considered in FMEA. Moreover, the relative weights of risk factors are derived objectively based on the idea of technique for order preference by similarity to ideal solution (TOPSIS) method. A practical risk evaluation case of aircraft landing system is given for verifying the applicability and effectiveness of the proposed FMEA. The results show that the proposed linguistic Z-number projection model is practical and flexible, which can not only depict experts' complex and uncertain risk evaluation information accurately, but also obtain more accurate risk prioritization of failure modes.
Bibliographie:ObjectType-Article-1
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2019.2955916