Risk evaluation model for failure mode and effect analysis using intuitionistic fuzzy rough number approach

Huang, Guangquan; Xiao, Liming

doi:10.1007/s00500-020-05497-0

Cited by 38 publications

(9 citation statements)

References 64 publications

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“…For example, the researches by 6,15,19 . However, with the increasing complexity of systems and the decision‐making environment, only three risk factors may not reveal the risk of failures 5 . In this respect, Lo, Liou 31 , and Chang 32 additionally considered the expected cost into the three classical risk factors to evaluate failure modes.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Xiao

Huang

Zhang

2022

Quality & Reliability Eng

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As a systematic risk assessment instrument, failure mode, and effects analysis (FMEA) has been extensively applied for enhancing systems’ quality. However, the classical FMEA technique has limitations in many application scenarios. Although fuzzy logic‐based risk assessment techniques are introduced to enhance conventional FMEA, they still have limitations, for example, needing prior presumptions, neglecting decision‐makers’ bounded rationality, and lacking the capability to reflect the judgmental reliability and randomness of experts’ evaluations simultaneously. To cover these limitations, an extended FMEA method is presented in this paper by using Z‐fuzzy clouds and generalized TODIM (An acronym in Portuguese of interactive and multiple attribute decision‐making). First, a novel concept of Z‐fuzzy clouds is proposed to handle various factors of uncertainties by combining the advantages of the Z‐number theory, the interval‐valued intuitionistic fuzzy sets theory, and the cloud model theory, and the basic operations and Minkowski‐type distance measure of Z‐fuzzy clouds are also defined and discussed. Furthermore, more risk factors are added into the conventional three risk factors and to further build a multiple dimension risk factor system for better evaluating failures. Additionally, the experts’ bounded rationality and the risk factors’ interrelationships are considered by two developed weighting models. Finally, a real study case, sensitivity analysis, and several comparisons are performed to validate the effectiveness of the proposed model. Results demonstrate that the proposed model is more rational compared with the previous several methods.

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Section: Literature Reviewmentioning

confidence: 99%

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Xiao

Huang

Zhang

2022

Quality & Reliability Eng

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“…Rough number [26] It can be used to deal with the drawback 6. in FMECA by aggregating the vague and uncertain evaluations of failure modes, which can reduce the subjectively of experts' opinion in aggregation process and make the decision-making more objective.…”

Section: Artificial Intelligence Techniquesmentioning

confidence: 99%

An Integrated Approach-Based FMECA for Risk Assessment: Application to Offshore Wind Turbine Pitch System

Wang

Deng

et al. 2022

Energies

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Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts’ diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members’ diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis.

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“…Moreover, the ranking for the selection of materials for mushroom cultivation was obtained through the MOORA method that was based on new score functions of interval-valued IFSs [28]. Some other aggregates of IFSs and rough numbers developed to cope with internal and external vagueness in evaluation of risks by Huang et al [29]. e proposed model utilized the rough numbers to handle external uncertainty, while IFSs have been used to deal with inner vagueness.…”

Section: Introductionmentioning

confidence: 99%

The Extended MOORA Method Based on Fermatean Fuzzy Information

Shahzadi

Luqman

Al-Shamiri

2022

Mathematical Problems in Engineering

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The intelligent manufacturing system (IMS) is a framework that improves productivity by organizing the logical features involved in manufacturing. The procedure of intelligent manufacturing owns the capability to self-control the manufacturing of the products according to the specifications of design. Different IMSs are designed to deal with continuous changes in market which can adjust to make the modified environment easier. The central idea of this research article is to select an IMS that can adapt the updated situations faster than the existing competing systems and provide higher benefits in utilizing new possibilities. To select such IMS, the applicability of multiobjective optimization on the basis of the ratio analysis (MOORA) method has been explored using Fermatean fuzzy sets. The Fermatean fuzzy aggregated weighted operators are used to construct the decision matrices. Then, the ratio analysis-based MOORA method is developed to accomplish the ranking of under consideration IMSs. Furthermore, the conversion of qualitative attributes into quantitative attributes has been performed using Fermatean fuzzy numbers (FFNs). Finally, a brief comparative analysis of the developed technique with existing models is narrated to reveal the flexibility of the Fermatean fuzzy MOORA method.

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Risk evaluation model for failure mode and effect analysis using intuitionistic fuzzy rough number approach

Cited by 38 publications

References 64 publications

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

An Integrated Approach-Based FMECA for Risk Assessment: Application to Offshore Wind Turbine Pitch System

The Extended MOORA Method Based on Fermatean Fuzzy Information

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