New failure mode and effect analysis approach considering consensus under interval-valued intuitionistic fuzzy environment

Abstract

As a powerful pre-accident risk evaluation method, the traditional failure mode and effect analysis (FMEA) is extensively used to identify and eliminate the potential failure modes of products or processes, and presents several limitations simultaneously. To improve the accuracy of risk evaluation, this paper proposes a novel FMEA approach considering consensus level between decision makers. First, linguistic variables are applied to express the decision makers’ evaluation information of failure modes, which can be transformed into the corresponding interval-valued intuitionistic fuzzy (IVIF) numbers. Second, an IVIF consensus model is constructed to confirm whether the consensus is achieved, and subsequently, the collective evaluation matrix is aggregated by the interval-valued intuitionistic fuzzy prioritized weighted averaging operator. Third, a deviation maximization model is used to calculate the weights of risk factors. Finally, the improved IVIF-MULTIMOORA method is implemented to determine the risk ranking of failure modes. This paper also provides a numerical example to illustrate the validity and rationality of the proposed method.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

References

  1. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    MATH  Article  Google Scholar 

  2. Atanassov KT (1994) Operators over interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst 64(2):159–174

    MathSciNet  MATH  Article  Google Scholar 

  3. Atanassov K, Gargov G (1989) Interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst 31(3):343–349

    MathSciNet  MATH  Article  Google Scholar 

  4. Bai ZY (2013) An interval-valued intuitionistic fuzzy TOPSIS method based on an improved score function. Sci World J Article ID 879089

  5. Baležentis T, Baležentis A (2014) A survey on development and applications of the multi-criteria decision making method MULTIMOORA. J Multi-Criteria Decis Anal 21(3–4):209–222

    Article  Google Scholar 

  6. Bowles JB, Peláez CE (1995) Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliab Eng Syst Saf 50(2):203–213

    Article  Google Scholar 

  7. Bozdag E, Asan U, Soyer A, Serdarasan S (2015) Risk prioritization in failure mode and effects analysis using interval type-2 fuzzy sets. Expert Syst Appl 42(8):4000–4015

    Article  Google Scholar 

  8. Brauers WKM (2012) Project management for a country with multiple objectives. Czech Econ Rev 6(1):80–102

    MathSciNet  Google Scholar 

  9. Brauers WKM, Zavadskas EK (2010) Project management by MULTIMOORA as an instrument for transition economies. Technol Econ Dev Econ 16(1):5–24

    Article  Google Scholar 

  10. Cabrerizo FJ, Heradio R, Pérez IJ, Herrera-Viedma E (2010) A selection process based on additive consistency to deal with incomplete fuzzy linguistic information. J Univ Comput Sci 16(1):62–81

    MathSciNet  MATH  Google Scholar 

  11. Chang KH (2014) A more general risk assessment methodology using a soft set-based ranking technique. Soft Comput 18(1):169–183

    Article  Google Scholar 

  12. Chang KH, Cheng CH (2010) A risk assessment methodology using intuitionistic fuzzy set in FMEA. Int J Syst Sci 41(12):1457–1471

    MathSciNet  Article  Google Scholar 

  13. Chang KH, Cheng CH (2011) Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. J Intell Manuf 22(2):113–129

    Article  Google Scholar 

  14. Chang CL, Wei CC, Lee YH (1999) Failure mode and effects analysis using fuzzy method and grey theory. Kybernetes 28(9):1072–1080

    Article  Google Scholar 

  15. Chin KS, Wang Y-M, Poon GKK, Yang JB (2009) Failure mode and effects analysis using a group-based evidential reasoning approach. Comput Oper Res 36(6):1768–1779

    MATH  Article  Google Scholar 

  16. Deshpande V, Modak J (2002) Application of RCM to a medium scale industry. Reliab Eng Syst Saf 77(1):31–43

    Article  Google Scholar 

  17. Du YX, Lu X, Su XY, Hu Y, Deng Y (2016) New failure mode and effects analysis: an evidential downscaling method. Qual Reliab Eng Int 32(2):737–746

    Article  Google Scholar 

  18. Emovon I, Norman RA, Alan JM, Pazouki K (2015) An integrated multicriteria decision making methodology using compromise solution methods for prioritising risk of marine machinery systems. Ocean Eng 105:92–103

    Article  Google Scholar 

  19. Franceschini F, Galetto M (2001) A new approach for evaluation of risk priorities of failure modes in FMEA. Int J Prod Res 39(13):2991–3002

    Article  Google Scholar 

  20. Herrera-Viedma E, Martinez L, Mata F, Chiclana F (2005) A consensus support system model for group decision-making problems with multigranular linguistic preference relations. IEEE Trans Fuzzy Syst 13(5):644–658

    Article  Google Scholar 

  21. Herrera-Viedma E, Cabrerizo FJ, Kacprzyk J, Pedrycz W (2014) A review of soft consensus models in a fuzzy environment. Inf Fus 17:4–13

    Article  Google Scholar 

  22. Hu AH, Hsu CW, Kuo TC, Wu WC (2009) Risk evaluation of green components to hazardous substance using FMEA and FAHP. Expert Syst Appl 36(3):7142–7147

    Article  Google Scholar 

  23. Ilangkumaran M, Shanmugam P, Sakthivel G, Visagavel K (2014) Failure mode and effect analysis using fuzzy analytic hierarchy process. Int J Prod Qual Manag 14(3):296–313

    Google Scholar 

  24. Kutlu AC, Ekmekçioğlu M (2012) Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst Appl 39(1):61–67

    Article  Google Scholar 

  25. Liu HC, Liu L, Bian QH, Lin QL, Dong N, Xu PC (2011) Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Syst Appl 38(4):4403–4415

    Article  Google Scholar 

  26. Liu HC, Liu L, Liu N (2013) Risk evaluation approaches in failure mode and effects analysis: a literature review. Expert Syst Appl 40(2):828–838

    Article  Google Scholar 

  27. Liu HC, Liu L, Li P (2014a) Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator. Int J Syst Sci 45(10):2012–2030

    MathSciNet  MATH  Article  Google Scholar 

  28. Liu HC, You JX, You XY (2014b) Evaluating the risk of healthcare failure modes using interval 2-tuple hybrid weighted distance measure. Comput Ind Eng 78:249–258

    Article  Google Scholar 

  29. Liu HC, Fan XJ, Li P, Chen YZ (2014c) Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment. Eng Appl Artif Intell 34:168–177

    Article  Google Scholar 

  30. Liu HC, Li P, You JX, Chen YZ (2015a) A novel approach for FMEA: combination of interval 2-tuple linguistic variables and gray relational analysis. Qual Reliab Eng Int 31(5):761–772

    Article  Google Scholar 

  31. Liu HC, You JX, You XY, Shan MM (2015b) A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method. Appl Soft Comput 28:579–588

    Article  Google Scholar 

  32. Liu HC, You JX, Chen S, Chen YZ (2016) An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty. IIE Trans 48(11):1027–1042

    Article  Google Scholar 

  33. Liu HC, You JX, Duan CY (2017) An integrated approach for failure mode and effect analysis under interval-valued intuitionistic fuzzy environment. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2017.03.008

    Article  Google Scholar 

  34. Mohsen O, Fereshteh N (2017) An extended VIKOR method based on entropy measure for the failure modes risk assessment—a case study of the geothermal power plant (GPP). Saf Sci 92:160–172

    Article  Google Scholar 

  35. Pillay A, Wang J (2003) Modified failure mode and effects analysis using approximate reasoning. Reliab Eng Syst Saf 79(1):69–85

    Article  Google Scholar 

  36. Safari H, Faraji Z, Majidian S (2016) Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR. J Intell Manuf 27(2):475–486

    Article  Google Scholar 

  37. Sharma RK, Kumar D, Kumar P (2005) Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. Int J Qual Reliab Manag 22(9):986–1004

    Article  Google Scholar 

  38. Song WY, Ming XG, Wu ZY, Zhu BT (2013) Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS. Int J Comput Integr Manuf 26(12):1172–1186

    Article  Google Scholar 

  39. Song WY, Ming XG, Wu ZY, Zhu BT (2014) A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Qual Reliab Eng Int 30(4):473–486

    Article  Google Scholar 

  40. Stamatis DH (2003) Failure mode and effect analysis: FMEA from theory to execution. ASQ Quality Press, Milwaukee

    Google Scholar 

  41. Tooranloo HS, Ayatollah AS (2016) A model for failure mode and effects analysis based on intuitionistic fuzzy approach. Appl Soft Comput 49:238–247

    Article  Google Scholar 

  42. Vahdani B, Salimi M, Charkhchian M (2015) A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process. Int J Adv Manuf Technol 77(1–4):357–368

    Article  Google Scholar 

  43. Wang XF, Zhang YZ, Shen GX (2016) An improved FMECA for feed system of CNC machining center based on ICR and DEMATEL method. Int J Adv Manuf Technol 83(1–4):43–54

    Article  Google Scholar 

  44. Wu ZB, Xu JP (2012) A consistency and consensus based decision support model for group decision making with multiplicative preference relations. Decis Support Syst 52(3):757–767

    Article  Google Scholar 

  45. Wu ZB, Xu JP (2016) Possibility distribution-based approach for MAGDM with hesitant fuzzy linguistic information. IEEE Trans Cybern 46(3):694–705

    Article  Google Scholar 

  46. Xu ZS (2007a) Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making. Control Decis 22(2):215–219

    MathSciNet  Google Scholar 

  47. Xu ZS (2007b) On similarity measures of interval-valued intuitionistic fuzzy sets and their application to pattern recognitions. J Southeast Univ (English Ed) 23(1):139–143

    MathSciNet  MATH  Google Scholar 

  48. Xu ZS (2010) A deviation-based approach to intuitionistic fuzzy multiple attribute group decision making. Group Decis Negot 19(1):57–76

    Article  Google Scholar 

  49. Xu JP, Wu ZB (2013) A maximizing consensus approach for alternative selection based on uncertain linguistic preference relations. Comput Ind Eng 64(4):999–1008

    Article  Google Scholar 

  50. Yager RR (2008) Prioritized aggregation operators. Int J Approx Reason 48(1):263–274

    MathSciNet  MATH  Article  Google Scholar 

  51. Yu DJ, Wu YY, Lu T (2012) Interval-valued intuitionistic fuzzy prioritized operators and their application in group decision making. Knowl Based Syst 30:57–66

    Article  Google Scholar 

  52. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Article  Google Scholar 

  53. Zhang X, Jin F, Liu PD (2013) A grey relational projection method for multi-attribute decision making based on intuitionistic trapezoidal fuzzy number. Appl Math Model 37(5):3467–3477

    MathSciNet  MATH  Article  Google Scholar 

  54. Zhao H, You JX, Liu HC (2017) Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment. Soft Comput 21(18):5355–5367

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by the National Natural Science Foundation of China (Nos. 71371156, 70971017) and Doctoral Innovation Fund Program of Southwest Jiaotong University (No. D-CX201727).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Rui Wang.

Ethics declarations

Conflict of Interest

The authors declare no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Communicated by V. Loia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Wang, R. & Chin, K. New failure mode and effect analysis approach considering consensus under interval-valued intuitionistic fuzzy environment. Soft Comput 23, 11611–11626 (2019). https://doi.org/10.1007/s00500-018-03706-5

Download citation

Keywords

  • Failure mode and effect analysis
  • Consensus model
  • Interval-valued intuitionistic fuzzy set
  • MULTIMOORA method
  • Risk evaluation