Journal ArticleDOI
Failure Mode and Effects Analysis by Using the House of Reliability-Based Rough VIKOR Approach
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TLDR
A new risk priority model is presented for FMEA by using the house of reliability (HoR)-based rough VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) approach and an illustrative case in transmission system of a vertical machining center has demonstrated the effectiveness and practicality of the proposed model.Abstract:
Failure mode and effects analysis (FMEA) is a widely used reliability analysis tool for identifying and eliminating known or potential failures in system, design, and process. In traditional FMEA, failure modes are evaluated by FMEA team members with respect to three risk factors: severity (S), occurrence (O), and detectability (D), and ranked via their risk priority number (RPN), which is obtained by multiplying the crisp values of S, O, and D. However, traditional RPN has been considerably criticized due to the following shortcomings: not considering the different weights of risk factors; the identical value of RPN for different combinations of S, O, and D; the diversity and uncertainty of evaluation information given by FMEA team members and without considering the dependence among different failure modes. Although significant efforts have been made in FMEA literatures to overcome these shortcomings, there are still some deficiencies. In this paper, a new risk priority model is presented for FMEA by using the house of reliability (HoR)-based rough VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) approach. In the proposed model, the HoR is introduced to identify the dependence among different failure modes and the link between failure modes and the risk factors of O, D and the subcriteria of S. Rough number is introduced to manipulate the subjectivity and vagueness in decision making and VIKOR approach is used to determine the risk priority order of failure modes in a comprehensive way. Finally, an illustrative case in transmission system of a vertical machining center has demonstrated the effectiveness and practicality of the proposed model.read more
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Journal ArticleDOI
Failure mode and effect analysis using multi-criteria decision making methods: A systematic literature review
TL;DR: This research supports academics and practitioners in effectively adopting MCDM methods to overcome the deficiencies of the traditional FMEA and provides an insight into its state-of-the-art.
Journal ArticleDOI
Failure Mode and Effect Analysis in a Linguistic Context: A Consensus-Based Multiattribute Group Decision-Making Approach
TL;DR: A consensus-based group decision-making framework for FMEA is proposed with the aim of classifying FMs into several ordinal risk classes in which it is assumed thatFMEA participants provide their preferences in a linguistic way using possibilistic hesitant fuzzy linguistic information.
Journal ArticleDOI
An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA
Soumava Boral,Soumava Boral,Ian Howard,Sanjay Chaturvedi,Kristoffer K. McKee,V. N. A. Naikan +5 more
TL;DR: This work proposes a novel integrated MCDM approach by combining Fuzzy Analytical Hierarchy Process (FAHP) with the modified FBuzzy Multi-Attribute Ideal Real Comparative Analysis (modified FMAIRCA), which is computationally inexpensive and able to provide more viable decisions.
Journal ArticleDOI
Risk Evaluation in Failure Mode and Effects Analysis Based on D Numbers Theory
Baoyu Liu,Yong Deng +1 more
TL;DR: A novel method to risk evaluation based on D numbers theory based on the combination usage of risk priority number (RPN) and the risk coefficient newly defined to achieve less computation complexity compared with other methods, but also overcome the shortcomings of classical RPN.
Journal ArticleDOI
Derive knowledge of Z-number from the perspective of Dempster–Shafer evidence theory
Qing Liu,Ye Tian,Bingyi Kang +2 more
TL;DR: The proposed method considers the Z-number generating from objective and subjective data using Dempster–Shafer theory, and considers it to be an effective frame to simulate the thinking of humans.
References
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Journal ArticleDOI
Rough sets
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Journal ArticleDOI
Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS
TL;DR: A comparative analysis of the multiple criteria decision making methods VIKOR and TOPSIS is illustrated with a numerical example, showing their similarity and some differences.
Journal ArticleDOI
Extensions of the TOPSIS for group decision-making under fuzzy environment
TL;DR: The rating of each alternative and the weight of each criterion are described by linguistic terms which can be expressed in triangular fuzzy numbers and a vertex method is proposed to calculate the distance between two triangular fuzzyNumbers.
Journal ArticleDOI
Extended VIKOR method in comparison with outranking methods
TL;DR: The VIKOR method as mentioned in this paper was developed to solve MCDM problems with conflicting and noncommensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria.
Journal ArticleDOI
Failure mode and effect analysis : FMEA from theory to execution
TL;DR: In this paper, failure mode and effect analysis: Failure Mode and Effect Analysis: FMEA From Theory to Execution Technometrics: Vol 38, No 1, pp 80-80
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