Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence

Failure mode and effects analysis is an effective and powerful risk evaluation technique in the field of risk management, and it has been extensively used in various industries for identifying and decreasing known and potential failure modes in systems, processes, products, and services. Traditionally, a risk priority number is applied to capture the ranking order of failure modes in failure mode and effects analysis. However, this method has several drawbacks and deficiencies, which need to be improved for enhancing its application capability. For instance, this method ignores the consensus-reaching process and the correlations among the experts’ preferences. Therefore, the aim of this study was to present a new risk priority method to determine the risk priority of failure modes under an interval-valued Pythagorean fuzzy environment, which combines the extended Geometric Bonferroni mean operator, a consensus-reaching process, and an improved Multi-Attributive Border Approximation area Comparison approach. Finally, a case study concerning product development is described to demonstrate the feasibility and effectiveness of the proposed method. The results show that the risk priority of failure modes obtained by the proposed method is more reasonable in practical application compared with other failure mode and effects analysis methods.

[1]  Haibin Liu,et al.  Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy Integral , 2017, Symmetry.

[2]  Hu-Chen Liu,et al.  Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment , 2014, Eng. Appl. Artif. Intell..

[3]  Yung-Chia Chang,et al.  Integrating TOPSIS and DEMATEL Methods to Rank the Risk of Failure of FMEA , 2014, Int. J. Inf. Technol. Decis. Mak..

[4]  Hu-Chen Liu,et al.  Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment , 2016, Soft Computing.

[5]  Yingzhi Zhang,et al.  An improved FMECA for feed system of CNC machining center based on ICR and DEMATEL method , 2016 .

[6]  Chunhe Xie,et al.  A modified method for risk evaluation in failure modes and effects analysis of aircraft turbine rotor blades , 2016 .

[7]  Ping Li,et al.  Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator , 2014, Int. J. Syst. Sci..

[8]  Xiaohong Chen,et al.  A Hesitant Fuzzy Linguistic Projection-Based MABAC Method for Patients’ Prioritization , 2017, International Journal of Fuzzy Systems.

[9]  Ming-Hung Shu,et al.  Extended FMEA for Sustainable Manufacturing: An Empirical Study in the Non-Woven Fabrics Industry , 2016 .

[10]  Hu-Chen Liu,et al.  Improving risk evaluation in FMEA with a hybrid multiple criteria decision making method , 2015 .

[11]  Loon Ching Tang,et al.  Fuzzy assessment of FMEA for engine systems , 2002, Reliab. Eng. Syst. Saf..

[12]  Omidvar Mohsen,et al.  An extended VIKOR method based on entropy measure for the failure modes risk assessment – A case study of the geothermal power plant (GPP) , 2017 .

[13]  Zhiming Zhang,et al.  Geometric Bonferroni means of interval-valued intuitionistic fuzzy numbers and their application to multiple attribute group decision making , 2018, Neural Computing and Applications.

[14]  H. Schneider.  Failure mode and effect analysis : FMEA from theory to execution , 1996 .

[15]  Zeshui Xu,et al.  Projection Model for Fusing the Information of Pythagorean Fuzzy Multicriteria Group Decision Making Based on Geometric Bonferroni Mean , 2017, Int. J. Intell. Syst..

[16]  Hu-Chen Liu,et al.  A Novel Approach for FMEA: Combination of Interval 2‐Tuple Linguistic Variables and Gray Relational Analysis , 2015, Qual. Reliab. Eng. Int..

[17]  Xinguo Ming,et al.  Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS , 2013, Int. J. Comput. Integr. Manuf..

[18]  Zhaojun Li,et al.  New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method , 2017, Reliab. Eng. Syst. Saf..

[19]  Nan Liu,et al.  Risk evaluation approaches in failure mode and effects analysis: A literature review , 2013, Expert Syst. Appl..

[20]  Zaifang Zhang,et al.  Risk prioritization in failure mode and effects analysis under uncertainty , 2011, Expert Syst. Appl..

[21]  Peide Liu,et al.  Multiple attribute decision-making method based on some normal neutrosophic Bonferroni mean operators , 2015, Neural Computing and Applications.

[22]  Erhan Bozdag,et al.  Risk prioritization in Failure Mode and Effects Analysis using interval type-2 fuzzy sets , 2015, Expert Syst. Appl..

[23]  Hu-Chen Liu,et al.  An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information , 2016, Appl. Soft Comput..

[24]  Rita Gamberini,et al.  FlowSort-GDSS - A novel group multi-criteria decision support system for sorting problems with application to FMEA , 2015, Expert Syst. Appl..

[25]  Zeshui Xu,et al.  Geometric Bonferroni means with their application in multi-criteria decision making , 2013, Knowl. Based Syst..

[26]  Hu-Chen Liu,et al.  Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments , 2016, Comput. Ind. Eng..

[27]  Xinyang Deng,et al.  A new method in failure mode and effects analysis based on evidential reasoning , 2014, International Journal of System Assurance Engineering and Management.

[28]  Fiorenzo Franceschini,et al.  A new approach for evaluation of risk priorities of failure modes in FMEA , 2001 .

[29]  Hu-Chen Liu,et al.  A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method , 2015, Appl. Soft Comput..

[30]  Guiwu Wei,et al.  Picture 2-Tuple Linguistic Bonferroni Mean Operators and Their Application to Multiple Attribute Decision Making , 2016, International Journal of Fuzzy Systems.

[31]  Na Hu,et al.  Multi-attribute group decision making method based on geometric Bonferroni mean operator of trapezoidal interval type-2 fuzzy numbers , 2015, Comput. Ind. Eng..

[32]  Dragan Pamučar,et al.  Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia , 2017 .

[33]  Jun Ye,et al.  Bonferroni Mean Operators of Linguistic Neutrosophic Numbers and Their Multiple Attribute Group Decision-Making Methods , 2017, Inf..

[34]  Qian Wang,et al.  Research on the assessment of psycholinguistic teaching effect with triangular fuzzy information , 2017, J. Intell. Fuzzy Syst..

[35]  Chee Peng Lim,et al.  A perceptual computing-based method to prioritize failure modes in failure mode and effect analysis and its application to edible bird nest farming , 2016, Appl. Soft Comput..

[36]  Hossein Safari,et al.  Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR , 2014, Journal of Intelligent Manufacturing.

[37]  Xiaolu Zhang,et al.  Multicriteria Pythagorean fuzzy decision analysis: A hierarchical QUALIFLEX approach with the closeness index-based ranking methods , 2016, Inf. Sci..

[38]  Francisco Herrera,et al.  A model of consensus in group decision making under linguistic assessments , 1996, Fuzzy Sets Syst..

[39]  Ahmet Can Kutlu,et al.  Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP , 2012, Expert Syst. Appl..

[40]  Guiwu Wei,et al.  Approaches to Multiple-Attribute Decision-Making Based on Pythagorean 2-Tuple Linguistic Bonferroni Mean Operators , 2018, Algorithms.

[41]  Hui Li,et al.  Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory , 2015, Int. J. Comput. Integr. Manuf..

[42]  Jurgita Antucheviciene,et al.  A Hybrid MCDM Approach for Strategic Project Portfolio Selection of Agro By-Products , 2017 .

[43]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

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

[45]  Francisco Herrera,et al.  A consensus model for multiperson decision making with different preference structures , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[46]  Dragan Pamucar,et al.  The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC) , 2015, Expert Syst. Appl..

[47]  Z. S. Xu,et al.  An overview of operators for aggregating information , 2003, Int. J. Intell. Syst..

[48]  Hu-Chen Liu,et al.  An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty , 2016 .

[49]  S. M. Seyed Hosseini,et al.  Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique , 2006, Reliab. Eng. Syst. Saf..

[50]  Yong Yang,et al.  Fundamental Properties of Interval‐Valued Pythagorean Fuzzy Aggregation Operators , 2016, Int. J. Intell. Syst..

[51]  Jian Guo.  A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory , 2016, J. Intell. Fuzzy Syst..

[52]  Pei Wang,et al.  Entropy, similarity measure of interval-valued intuitionistic fuzzy sets and their applications , 2011, Inf. Sci..

[53]  Eric Châtelet,et al.  Using a Hybrid Cost-FMEA Analysis for Wind Turbine Reliability Analysis , 2017 .