Skip to main content
Log in

Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Failure mode and effects analysis (FMEA) is an effective reliability analysis technique used to identify and evaluate potential failures in systems, products, processes, and/or designs. In traditional FMEA, prioritization of failure modes is carried out by utilizing risk priority numbers (RPNs), which can be acquired by the multiplication of three risk factors: occurrence (O), severity (S) and detection (D). However, there are some inherent deficiencies in the conventional RPN method, which affect its effectiveness and thus limit its applications. In response, this paper introduces a new modified TOPSIS method, named intuitionistic fuzzy hybrid TOPSIS approach, to determine the risk priorities of failure modes identified in FMEA. Moreover, both the subjective and objective weights of risk factors are taken into consideration in the process of risk and failure analysis. A product example of the color super twisted nematic is presented at last to demonstrate the potential applications of the proposed approach, and the merits are highlighted by comparing with some existing methods.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

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

    Article  MATH  MathSciNet  Google Scholar 

  • Boran FE, Genç S, Kurt M, Akay D (2009) A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl 36(8):11363–11368

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Braglia M, Frosolini M, Montanari R (2003) Fuzzy TOPSIS approach for failure mode, effects and criticality analysis. Qual Reliab Eng Int 19(5):425–443

    Article  Google Scholar 

  • 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 

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

  • Chang KH, Wen TC (2010) A novel efficient approach for DFMEA combining 2-tuple and the OWA operator. Expert Syst Appl 37(3):2362–2370

    Article  Google Scholar 

  • Chang KH, Cheng CH, Chang YC (2010) Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique. Soft Comput 14(3):285–298

    Article  MathSciNet  Google Scholar 

  • Chang KH, Chang YC, Tsai IT (2013) Enhancing FMEA assessment by integrating grey relational analysis and the decision making trial and evaluation laboratory approach. Eng Fail Anal 31:211–224

    Article  Google Scholar 

  • Chen SJ, Hwang CL, Hwang FP (1992) Fuzzy multiple attribute decision making: methods and applications. Springer, Berlin

    Book  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Du Y, Mo H, Deng X, Sadiq R, Deng Y (2014) A new method in failure mode and effects analysis based on evidential reasoning. Int J Syst Assur Eng Manag 5(1):1–10

    Article  Google Scholar 

  • Gargama H, Chaturvedi SK (2011) Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Trans Reliab 60(1):102–110

    Article  Google Scholar 

  • Hadi-Vencheh A, Hejazi S, Eslaminasab Z (2013) A fuzzy linear programming model for risk evaluation in failure mode and effects analysis. Neural Comput Appl 22(6):1105–1113

    Article  Google Scholar 

  • Helvacioglu S, Ozen E (2014) Fuzzy based failure modes and effect analysis for yacht system design. Ocean Eng 79:131–141

    Article  Google Scholar 

  • Hwang CL, Yoon K (1981) Multiple attributes decision making methods and applications. Springer, Berlin

    Book  Google Scholar 

  • Kahraman C, Kaya İ, Şenvar Ö (2013) Healthcare failure mode and effects analysis under fuzziness. Hum Ecol Risk Assess 19(2):538–552

    Article  Google Scholar 

  • 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 

  • Lin QL, Wang DJ, Lin WG, Liu HC (2014) Human reliability assessment for medical devices based on failure mode and effects analysis and fuzzy linguistic theory. Saf Sci 62:248–256

    Article  Google Scholar 

  • 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 

  • Liu HC, Liu L, Liu N, Mao LX (2012) Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Syst Appl 39(17):12926–12934

    Article  MathSciNet  Google Scholar 

  • Liu HC, Liu L, Li P (2013a) Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator. Int J Syst Sci. doi:10.1080/00207721.2012.760669

  • Liu HC, Liu L, Lin QL (2013b) Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology. IEEE Trans Reliab 62(1):23–36

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Liu HC, Li P, You JX, Chen YZ (2014a) A novel approach for FMEA: combination of interval 2-tuple linguistic variables and grey relational analysis. Qual Reliab Eng Int. doi:10.1002/qre.1633

  • Liu HC, Ren ML, Wu J, Lin QL (2014b) An interval 2-tuple linguistic MCDM method for robot evaluation and selection. Int J Prod Res 52(10):2867–2880

    Article  Google Scholar 

  • Liu HC, You JX, Fan XJ, Lin QL (2014c) Failure mode and effects analysis using D numbers and grey relational projection method. Expert Syst Appl 41(10):4670–4679

    Article  Google Scholar 

  • Liu HC, You JX, Lin QL, Li H (2014d) Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision making trial and evaluation laboratory. Int J Comput Integr Manuf. doi:10.1080/0951192X.2014.900865

  • Mandal S, Maiti J (2014) Risk analysis using FMEA: fuzzy similarity value and possibility theory based approach. Expert Syst Appl 41(7):3527–3537

    Article  Google Scholar 

  • Merigó JM, Gil Lafuente AM (2008) The generalized adequacy coefficient and its application in strategic decision making. Fuzzy Econ Rev 13(2):17–36

    Google Scholar 

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

    Article  Google Scholar 

  • Sang X, Liu X (2014) An analytic approach to obtain the least square deviation OWA operator weights. Fuzzy Sets Syst 240:103–116

    Article  MathSciNet  Google Scholar 

  • Seyed-Hosseini SM, Safaei N, Asgharpour MJ (2006) Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique. Reliab Eng Syst Safe 91(8):872–881

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Song W, Ming X, Wu Z, Zhu B (2013) Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS. Int J Comput Integr Manuf 26(12):1172–1186

  • Song W, Ming X, Wu Z, Zhu B (2014) A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Qual Reliab Eng Int 30(4):473–486

  • Stamatis DH (2003) Failure mode and effect analysis: FMEA from theory to execution, 2nd edn. ASQC Quality Press, Milwaukee

    Google Scholar 

  • Tay KM, Lim CP (2006) Fuzzy FMEA with a guided rules reduction system for prioritization of failures. Int J Qual Reliab Manage 23(8):1047–1066

    Article  Google Scholar 

  • Teoh PC, Case K (2005) An evaluation of failure modes and effects analysis generation method for conceptual design. Int J Comput Integ Manuf 18(4):279–293

    Article  Google Scholar 

  • Vahdani B, Tavakkoli-Moghaddam R, Mousavi SM, Ghodratnama A (2013) Soft computing based on new interval-valued fuzzy modified multi-criteria decision-making method. Appl Soft Comput 13(1):165–172

    Article  Google Scholar 

  • Vinodh S, Aravindraj S, Narayanan RS, Yogeshwaran N (2012) Fuzzy assessment of FMEA for rotary switches: a case study. TQM J 24(5):461–475

    Article  Google Scholar 

  • Wang YM, Chin KS, Poon GKK, Yang JB (2009) Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert Syst Appl 36(2):1195–1207

    Article  Google Scholar 

  • Xu ZS (2005) An overview of methods for determining OWA weights. Int J Intell Syst 20(8):843–865

    Article  MATH  Google Scholar 

  • Xu ZS (2007) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15(6):1179–1187

    Article  Google Scholar 

  • Xu ZS, Yager RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35(4):417–433

    Article  MATH  MathSciNet  Google Scholar 

  • Xu ZS, Yager RR (2008) Dynamic intuitionistic fuzzy multi-attribute decision making. Int J Approx Reason 48(1):246–262

    Article  MATH  MathSciNet  Google Scholar 

  • Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  MATH  MathSciNet  Google Scholar 

  • Yang JP, Huang HZ, He LP, Zhu SP, Wen DW (2011) Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster-Shafer evidence theory under uncertainty. Eng Fail Anal 18(8):2084–2092

    Article  Google Scholar 

  • Yeh TM, Chen LY (2013) Fuzzy-based risk priority number in FMEA for semiconductor wafer processes. Int J Prod Res 52(2):539–549

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors sincerely thank the editor and the anonymous reviewers for their insights and helpful comments and suggestions which are very helpful in improving the quality of the paper. This work was supported by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning and the National Natural Science Foundation of China (No. 71101087).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lu-Ning Shao.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, HC., You, JX., Shan, MM. et al. Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach. Soft Comput 19, 1085–1098 (2015). https://doi.org/10.1007/s00500-014-1321-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1321-x

Keywords

Navigation