An FMEA Risk Assessment Method Based on Social Networks Considering Expert Clustering and Risk Attitudes

Failure mode and effect analysis (FMEA) method has been widely utilized to solve the problem of risk assessment in all walks of life. An FMEA decision support model considering expert clustering and risk attitude is constructed. First, expert risk assessment information is processed in cloud environment. The clustering behavior of experts is simulated based on trust relationship, opinion similarity, and risk attitude similarity. Second, consensus opinions are formed through opinion evolution, and the group weight determination model is constructed considering the group size and consensus level. Finally, a linear programming model minimizing individual regret is used to solve the risk factor (RF) weight problem. Combined with regret theory and the TODIM method considering finite rationality, the priority of risk is determined. The novel FMEA approach is applied to address reliability management problem of smart bracelets. Sensitivity and comparative analyses demonstrated the effectiveness and superiority of this method and enrich the theoretical research of the FMEA approach.

[1]  Deyun Zhou,et al.  Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method , 2023, Complex & Intelligent Systems.

[2]  Deyun Zhou,et al.  Conflicting evidence fusion using a correlation coefficient-based approach in complex network , 2023, Chaos, Solitons & Fractals.

[3]  Peide Liu,et al.  An Improved Failure Mode and Effect Analysis Model for Automatic Transmission Risk Assessment Considering the Risk Interaction , 2023, IEEE Transactions on Reliability.

[4]  Yubo Huang,et al.  An improved risk priority number model for FMEA based on belief measure , 2023, 2023 35th Chinese Control and Decision Conference (CCDC).

[5]  Zhen Hua,et al.  An ELICIT information-based ORESTE method for failure mode and effect analysis considering risk correlation with GRA-DEMATEL , 2023, Inf. Fusion.

[6]  Gang Li,et al.  An asymmetric cost consensus based failure mode and effect analysis method with personalized risk attitude information , 2023, Reliab. Eng. Syst. Saf..

[7]  D. Zhou,et al.  Canonical triangular interval type-2 fuzzy set linguistic distribution assessment TODIM approach: A case study of FMEA for electric vehicles DC charging piles , 2023, Expert Syst. Appl..

[8]  Shibo Wu,et al.  Failure mode and effects analysis based on rough cloud model and MULTIMOORA method: Application to single-point mooring system , 2022, Appl. Soft Comput..

[9]  Duojin Wang,et al.  Risk assessment based on FMEA combining DEA and cloud model: A case application in robot-assisted rehabilitation , 2022, Expert Syst. Appl..

[10]  Hu-Chen Liu,et al.  A New Model for Failure Mode and Effects Analysis Based on k-Means Clustering Within Hesitant Linguistic Environment , 2022, IEEE Transactions on Engineering Management.

[11]  Liguo Fei,et al.  An improved risk prioritization method for propulsion system based on heterogeneous information and PageRank algorithm , 2022, Expert Syst. Appl..

[12]  Peide Liu,et al.  An integrated TOPSIS–ORESTE-based decision-making framework for new energy investment assessment with cloud model , 2022, Computational and Applied Mathematics.

[13]  Morteza Yazdani,et al.  A novel hesitant-fuzzy-based group decision approach for outsourcing risk , 2021, Expert Syst. Appl..

[14]  Liu Jiahao,et al.  Risk assessment of submarine pipelines using modified FMEA approach based on cloud model and extended VIKOR method , 2021, Process Safety and Environmental Protection.

[15]  Peiwu Dong,et al.  Identifying critical causal criteria of green supplier evaluation using heterogeneous judgements: An integrated approach based on cloud model and DEMATEL , 2021, Appl. Soft Comput..

[16]  C. Guedes Soares,et al.  A failure analysis of floating offshore wind turbines using AHP-FMEA methodology , 2021, Ocean Engineering.

[17]  Mohammad Mahdi Paydar,et al.  Reaching sustainability through collection center selection considering risk: using the integration of Fuzzy ANP-TOPSIS and FMEA , 2021, Soft Computing.

[18]  Yan Song,et al.  Selection of product recycling channels based on extended TODIM method , 2020, Expert Syst. Appl..

[19]  Yanbing Ju,et al.  A new approach for heterogeneous linguistic failure mode and effect analysis with incomplete weight information , 2020, Comput. Ind. Eng..

[20]  Muhammad Akram,et al.  Risk evaluation in failure modes and effects analysis: hybrid TOPSIS and ELECTRE I solutions with Pythagorean fuzzy information , 2020, Neural Computing and Applications.

[21]  Xinwang Liu,et al.  Failure Mode and Effect Analysis for Machine Tool Risk Analysis Using Extended Gained and Lost Dominance Score Method , 2020, IEEE Transactions on Reliability.

[22]  Hu-Chen Liu,et al.  Failure mode and effect analysis improvement: A systematic literature review and future research agenda , 2020, Reliab. Eng. Syst. Saf..

[23]  Dongdong Wu,et al.  An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory , 2020, Qual. Reliab. Eng. Int..

[24]  Feng Li,et al.  A novel approach to emergency risk assessment using FMEA with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment , 2020, Int. J. Intell. Comput. Cybern..

[25]  Xuanhua Xu,et al.  Consensus-based non-cooperative behaviors management in large-group emergency decision-making considering experts' trust relations and preference risks , 2020, Knowl. Based Syst..

[26]  Xinlong Li,et al.  A failure mode and risk assessment method based on cloud model , 2019, Journal of Intelligent Manufacturing.

[27]  Xiaojie Guo,et al.  An Improved Assessment Method for FMEA for a Shipboard Integrated Electric Propulsion System Using Fuzzy Logic and DEMATEL Theory , 2019, Energies.

[28]  Minghe Sun,et al.  Failure Mode and Effects Analysis Using Two-Dimensional Uncertain Linguistic Variables and Alternative Queuing Method , 2019, IEEE Transactions on Reliability.

[29]  Hu-Chen Liu,et al.  Failure mode and effect analysis with extended grey relational analysis method in cloud setting , 2019 .

[30]  Jindong Qin,et al.  An extended generalized TODIM for risk evaluation and prioritization of failure modes considering risk indicators interaction , 2019, IISE Trans..

[31]  Hu-Chen Liu,et al.  A linguistic risk prioritization approach for failure mode and effects analysis: A case study of medical product development , 2019, Qual. Reliab. Eng. Int..

[32]  Hu-Chen Liu,et al.  Improving Risk Evaluation in FMEA With Cloud Model and Hierarchical TOPSIS Method , 2019, IEEE Transactions on Fuzzy Systems.

[33]  Zhang-peng Tian,et al.  An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods , 2018, Appl. Soft Comput..

[34]  Mohammad Yazdi,et al.  Improving failure mode and effect analysis (FMEA) with consideration of uncertainty handling as an interactive approach , 2018, International Journal on Interactive Design and Manufacturing (IJIDeM).

[35]  Wei Zheng,et al.  Failure Mode and Effects Analysis by Using the House of Reliability-Based Rough VIKOR Approach , 2018, IEEE Transactions on Reliability.

[36]  Mohammad A. Khalilzadeh,et al.  Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment , 2018, Safety Science.

[37]  Hu-Chen Liu,et al.  An integrated approach for failure mode and effect analysis under interval-valued intuitionistic fuzzy environment , 2017, International Journal of Production Economics.

[38]  Hossein Sayyadi Tooranloo,et al.  A model for failure mode and effects analysis based on intuitionistic fuzzy approach , 2016, Appl. Soft Comput..

[39]  Hu-Chen Liu,et al.  Failure Mode and Effect Analysis Under Uncertainty: An Integrated Multiple Criteria Decision Making Approach , 2016, IEEE Transactions on Reliability.

[40]  Xia Feng,et al.  A multi-risks group evaluation method for the informatization project under linguistic environment , 2014, J. Intell. Fuzzy Syst..

[41]  B. Farhadinia,et al.  Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets , 2013, Inf. Sci..

[42]  Rainer Hegselmann,et al.  Opinion dynamics and bounded confidence: models, analysis and simulation , 2002, J. Artif. Soc. Soc. Simul..

[43]  Palma Blonda,et al.  A survey of fuzzy clustering algorithms for pattern recognition. I , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[44]  Dimitar Filev,et al.  Induced ordered weighted averaging operators , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[45]  R. Yager.  Quantifier guided aggregation using OWA operators , 1996, Int. J. Intell. Syst..

[46]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[47]  David E. Bell,et al.  Regret in Decision Making under Uncertainty , 1982, Oper. Res..

[48]  Decui Liang,et al.  Risk Assessment in Failure Mode and Effect Analysis: Improved ORESTE Method With Hesitant Pythagorean Fuzzy Information , 2021, IEEE Transactions on Engineering Management.

[49]  Enrique Herrera-Viedma,et al.  Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts , 2021, Reliab. Eng. Syst. Saf..

[50]  Peide Liu,et al.  An improved failure mode and effect analysis method for multi-criteria group decision-making in green logistics risk assessment , 2021, Reliab. Eng. Syst. Saf..

[51]  Yiyi Zhao,et al.  Fuzzy inference based Hegselmann-Krause opinion dynamics for group decision-making under ambiguity , 2021, Inf. Process. Manag..

[52]  Lin Li,et al.  A novel risk assessment model based on failure mode and effect analysis and probabilistic linguistic ELECTRE II method , 2020, J. Intell. Fuzzy Syst..

[53]  J. B. Bowles,et al.  Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis , 1995 .