Failure mode and effects analysis by integrating Bayesian fuzzy assessment number and extended gray relational analysis‐technique for order preference by similarity to ideal solution method

Failure mode and effects analysis (FMEA) is a prospective risk assessment tool used to identify, assess, and eliminate potential failure modes (FMs) in various industries to improve security and reliability. However, the traditional FMEA method has been criticized for several shortcomings and even t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Quality and reliability engineering international Jg. 35; H. 6; S. 1676 - 1697
Hauptverfasser: Nie, Wenbin, Liu, Weidong, Wu, Zhongyi, Chen, Binsong, Wu, Lulu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.10.2019
Schlagworte:
Bayesian analysis
Bayesian fuzzy assessment number
Compressor blades
Failure analysis
failure mode and effects analysis
Failure modes
GRA‐TOPSIS
Hausdorff distance
Metric space
probability density function
Probability density functions
Risk assessment
Similarity
Turbines
ISSN:0748-8017, 1099-1638
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Failure mode and effects analysis (FMEA) is a prospective risk assessment tool used to identify, assess, and eliminate potential failure modes (FMs) in various industries to improve security and reliability. However, the traditional FMEA method has been criticized for several shortcomings and even the improved FMEA methods based on predefined linguistic terms cannot meet the needs of FMEA team members' diversified opinion expressions. To solve these problems, a novel FMEA method is proposed by integrating Bayesian fuzzy assessment number (BFAN) and extended gray relational analysis‐technique for order preference by similarity to ideal solution (GRA‐TOPSIS) method. First, the BFANs are used to flexibly describe the risk evaluation results of the identified failure modes. Second, the Hausdorff distance between BFANs is calculated by using the probability density function (PDF). Finally, on the basis of the distance, the extended GRA‐TOPSIS method is applied to prioritize failure modes. A simulation study is presented to verify the effectiveness of the proposed approach in dealing with vague concepts and show its advantages over existing FMEA methods. Furthermore, a real case concerning the risk evaluation of aero‐engine turbine and compressor blades is provided to illustrate the practical application of the proposed method and particularly show the potential of using the BFANs in capturing FMEA team members' diverse opinions.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0748-8017
1099-1638
DOI:10.1002/qre.2468