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Granulized Z-VIKOR Model for Failure Mode and Effect Analysis
- Source :
- IEEE Transactions on Fuzzy Systems. 30:297-309
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- In this study, we have developed an improved failure mode and effect analysis (FMEA) model by leveraging the concepts of Z-number, rough number (RN), and probabilistic distance measure. Two new concepts, namely, double upper approximated rough number (DUARN) and granulized Z-number (gZN), a new scheme for measuring distance between two gZNs using weighted similarity and average linkage method, a new risk prioritization model, named, granulized Z-VIKOR, and a scheme for uncertainty assessment using box-plot are proposed. DUARN embodies the notion of double sided upper approximation of an ordinal decision class. gZN is developed using Z-number and DUARN. The distance between two gZNs is computed using the maximum entropy principle that captures the relationship between the A (opinion) and B (reliability of A) parts of gZN. The granulized Z-VIKOR involves synergistic integration of gZN and VIKOR. Both objective and subjective risk measures are computed and a combined risk measure is defined, which considers the interactions among the risk criteria using λ-Shapley index. Two case studies are conducted. Sensitivity & comparative analysis is carried out to demonstrate the applicability, effectiveness and robustness of the proposed model, as well as its superiority to existing models.
- Subjects :
- Mathematical optimization
Applied Mathematics
Principle of maximum entropy
Risk measure
Probabilistic logic
Rough number
02 engineering and technology
Measure (mathematics)
Computational Theory and Mathematics
Similarity (network science)
Artificial Intelligence
Control and Systems Engineering
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Failure mode and effects analysis
Mathematics
Subjects
Details
- ISSN :
- 19410034 and 10636706
- Volume :
- 30
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Fuzzy Systems
- Accession number :
- edsair.doi...........4bfdcb2eeb4d48d9b02c6cb6dfed6bb7
- Full Text :
- https://doi.org/10.1109/tfuzz.2020.3037933