Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach

详细信息    查看全文

• 作者：Hu-Chen Liu (1) (2)
  Jian-Xin You (1) (2)
  Meng-Meng Shan (2)
  Lu-Ning Shao (1)
  
  1. School of Economics and Management ; Tongji University ; 1239 Siping Road ; Shanghai ; 200092 ; People鈥檚 Republic of China
  2. School of Management ; Shanghai University ; Shanghai ; 200444 ; People鈥檚 Republic of China
  
• 关键词：Failure mode and effects analysis ; Intuitionistic fuzzy set ; OWA operator ; Modified TOPSIS
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：19
• 期：4
• 页码：1085-1098
• 全文大小：673 KB
• 参考文献：1. Atanassov, KT (1986) Intuitionistic fuzzy sets. Fuzzy Set Syst 20: pp. 87-96 CrossRef
  2. Boran, FE, Gen莽, S, Kurt, M, Akay, D (2009) A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl 36: pp. 11363-11368 CrossRef
  3. Bowles, JB, Pel谩ez, CE (1995) Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliab Eng Syst Safe 50: pp. 203-213 CrossRef
  4. Braglia, M, Frosolini, M, Montanari, R (2003) Fuzzy TOPSIS approach for failure mode, effects and criticality analysis. Qual Reliab Eng Int 19: pp. 425-443 CrossRef
  5. Chang, KH (2014) A more general risk assessment methodology using a soft set-based ranking technique. Soft Comput 18: pp. 169-183 CrossRef
  6. Chang KH, Cheng CH (2010) A risk assessment methodology using intuitionistic fuzzy set in FMEA. Int J Syst Sci 41(12):1457鈥?1471
  7. Chang, KH, Wen, TC (2010) A novel efficient approach for DFMEA combining 2-tuple and the OWA operator. Expert Syst Appl 37: pp. 2362-2370 CrossRef
  8. 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: pp. 285-298 CrossRef
  9. 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: pp. 211-224 CrossRef
  10. Chen, SJ, Hwang, CL, Hwang, FP (1992) Fuzzy multiple attribute decision making: methods and applications. Springer, Berlin CrossRef
  11. 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: pp. 1768-1779 CrossRef
  12. 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: pp. 1-10 CrossRef
  13. Gargama, H, Chaturvedi, SK (2011) Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Trans Reliab 60: pp. 102-110 CrossRef
  14. 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: pp. 1105-1113 CrossRef
  15. Helvacioglu, S, Ozen, E (2014) Fuzzy based failure modes and effect analysis for yacht system design. Ocean Eng 79: pp. 131-141 CrossRef
  16. Hwang, CL, Yoon, K (1981) Multiple attributes decision making methods and applications. Springer, Berlin CrossRef
  17. Kahraman, C, Kaya, 陌, 艦envar, 脰 (2013) Healthcare failure mode and effects analysis under fuzziness. Hum Ecol Risk Assess 19: pp. 538-552 CrossRef
  18. Kutlu, AC, Ekmek莽io臒lu, M (2012) Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst Appl 39: pp. 61-67 CrossRef
  19. 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: pp. 248-256 CrossRef
  20. 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: pp. 4403-4415 CrossRef
  21. 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: pp. 12926-12934 CrossRef
  22. 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
  23. Liu, HC, Liu, L, Lin, QL (2013) Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology. IEEE Trans Reliab 62: pp. 23-36 CrossRef
  24. Liu, HC, Liu, L, Liu, N (2013) Risk evaluation approaches in failure mode and effects analysis: a literature review. Expert Syst Appl 40: pp. 828-838 CrossRef
  25. 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
  26. Liu, HC, Ren, ML, Wu, J, Lin, QL (2014) An interval 2-tuple linguistic MCDM method for robot evaluation and selection. Int J Prod Res 52: pp. 2867-2880 CrossRef
  27. Liu, HC, You, JX, Fan, XJ, Lin, QL (2014) Failure mode and effects analysis using D numbers and grey relational projection method. Expert Syst Appl 41: pp. 4670-4679 CrossRef
  28. 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
  29. Mandal, S, Maiti, J (2014) Risk analysis using FMEA: fuzzy similarity value and possibility theory based approach. Expert Syst Appl 41: pp. 3527-3537 CrossRef
  30. Merig贸, JM, Gil Lafuente, AM (2008) The generalized adequacy coefficient and its application in strategic decision making. Fuzzy Econ Rev 13: pp. 17-36
  31. Pillay, A, Wang, J (2003) Modified failure mode and effects analysis using approximate reasoning. Reliab Eng Syst Safe 79: pp. 69-85 CrossRef
  32. Sang, X, Liu, X (2014) An analytic approach to obtain the least square deviation OWA operator weights. Fuzzy Sets Syst 240: pp. 103-116 CrossRef
  33. 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: pp. 872-881 CrossRef
  34. Sharma, RK, Kumar, D, Kumar, P (2005) Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. Int J Qual Reliab Manage 22: pp. 986-1004 CrossRef
  35. 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鈥?186
  36. 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鈥?86
  37. Stamatis, DH (2003) Failure mode and effect analysis: FMEA from theory to execution. ASQC Quality Press, Milwaukee
  38. Tay, KM, Lim, CP (2006) Fuzzy FMEA with a guided rules reduction system for prioritization of failures. Int J Qual Reliab Manage 23: pp. 1047-1066 CrossRef
  39. Teoh, PC, Case, K (2005) An evaluation of failure modes and effects analysis generation method for conceptual design. Int J Comput Integ Manuf 18: pp. 279-293 CrossRef
  40. 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: pp. 165-172 CrossRef
  41. Vinodh, S, Aravindraj, S, Narayanan, RS, Yogeshwaran, N (2012) Fuzzy assessment of FMEA for rotary switches: a case study. TQM J 24: pp. 461-475 CrossRef
  42. 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: pp. 1195-1207 CrossRef
  43. Xu, ZS (2005) An overview of methods for determining OWA weights. Int J Intell Syst 20: pp. 843-865 CrossRef
  44. Xu, ZS (2007) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15: pp. 1179-1187 CrossRef
  45. Xu, ZS, Yager, RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35: pp. 417-433 CrossRef
  46. Xu, ZS, Yager, RR (2008) Dynamic intuitionistic fuzzy multi-attribute decision making. Int J Approx Reason 48: pp. 246-262 CrossRef
  47. Yager, RR (1988) On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans Syst Man Cybern 18: pp. 183-190 CrossRef
  48. 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: pp. 2084-2092 CrossRef
  49. Yeh, TM, Chen, LY (2013) Fuzzy-based risk priority number in FMEA for semiconductor wafer processes. Int J Prod Res 52: pp. 539-549 CrossRef
  50. Zadeh, LA (1965) Fuzzy sets. Inf Control 8: pp. 338-353 CrossRef
  
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

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.