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Soft Computing

, Volume 18, Issue 1, pp 169–183 | Cite as

A more general risk assessment methodology using a soft set-based ranking technique

  • Kuei-Hu Chang
Methodologies and Application

Abstract

Process failure mode and effects analysis (PFMEA) is used in the high-tech industry to improve a product’s quality and robustness. It is not only an important risk assessment technique but also a valuable task for implementing production management. Its main purpose is to discover and prioritize potential failure modes. Most of the current PFMEA techniques use the risk priority number (RPN) value to evaluate the risk of failure. However, the traditional RPN methodology has a serious problem with regard to measurement scales, does not consider the direct and indirect relationship between potential failure modes and causes of failure, and loses potentially valuable expert-provided information. Moreover, there are unknown, partially known, missing, or nonexistent data identified during the process of collecting data for PFMEA; this increases the difficulty of risk assessment. Issues with incomplete information cannot be fully addressed using the traditional RPN methodology. In order to effectively address this problem, the current paper proposes a novel soft set-based ranking technique for the prioritization of failures in a product PFMEA. For verification of the proposed approach, a numerical example of the Xtal unit PFMEA was adopted. This study also compares the results of the traditional RPN and DEMATEL methods for dealing with incomplete data. The results demonstrate that the proposed approach is preferable for reflecting actual stages of incomplete data in PFMEA. As a result, product and process robustness can be assured.

Keywords

Soft set Decision making trial and evaluation laboratory Risk priority number Process failure mode and effects analysis Risk assessment 

Notes

Acknowledgments

The author would like to express his sincerest gratitude to the Associate Editor and the anonymous referees for providing very helpful comments and suggestions which led to an improvement of the article. This work was supported in part by the National Science Council of the Republic of China under Contract No. NSC 99-2410-H-145-001 and NSC 101-2410-H-145-001.

References

  1. Aktas H, Cagman N (2007) Soft sets and soft groups. Inf Sci 177:2726–2735CrossRefzbMATHMathSciNetGoogle Scholar
  2. Bowles JB (2003) An assessment of RPN prioritization in a failure modes effects and criticality analysis. In: Processing annual reliability and maintainability symposium, pp 380–386Google Scholar
  3. Bowles JB, Pelaez CE (1995) Fuzzy logic prioritization of failures in a system failure modes, effects and criticality analysis. Reliab Eng Syst Saf 50:203–213CrossRefGoogle Scholar
  4. Braglia M (2000) MAFMA: multi-attribute failure mode analysis. Int J Qual Reliab Manage 17:1017–1033CrossRefGoogle Scholar
  5. Braglia M, Frosolini M, Montanari R (2003) Fuzzy criticality assessment model for failure modes and effects analysis. Int J Qual Reliab Manage 20:503–524CrossRefGoogle Scholar
  6. Cagman N, Enginoglu S (2010) Soft set theory and uni-int decision making. Eur J Oper Res 207:848–855CrossRefzbMATHMathSciNetGoogle Scholar
  7. Carmignani G (2009) An integrated structural framework to cost-based FMECA: the priority-cost FMECA. Reliab Eng Syst Saf 94:861–871CrossRefGoogle Scholar
  8. Cassanelli G, Mura G, Fantini F, Vanzi M, Plano B (2006) Failure analysis-assisted FMEA. Microelectron Reliab 46:1795–1799CrossRefGoogle Scholar
  9. Chang KH (2009) Evaluate the orderings of risk for failure problems using a more general RPN methodology. Microelectron Reliab 49:1586–1596CrossRefGoogle Scholar
  10. Chang KH, Cheng CH (2010) A risk assessment methodology using intuitionistic fuzzy set in FMEA. Int J Syst Sci 41:1457–1471CrossRefMathSciNetGoogle Scholar
  11. Chang KH, Cheng CH (2011) Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. J Intell Manuf 22:113–129CrossRefGoogle Scholar
  12. Chang KH, Wen TC (2010) A novel efficient approach for DFMEA combining 2-tuple and the OWA operator. Expert Syst Appl 37:2362–2370CrossRefGoogle Scholar
  13. Chang KH, Cheng CH, Chang YC (2008) Reliability assessment of an aircraft propulsion system using IFS and OWA tree. Eng Optimiz 40:907–921CrossRefMathSciNetGoogle Scholar
  14. Chang KH, Cheng CH, Chang YC (2010) Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique. Soft Comput 14:285–298CrossRefGoogle Scholar
  15. Chang KH, Chang YC, Wen TC, Cheng CH (2012) An innovative approach integrating 2-tuple and LOWGA operators in process failure mode and effects analysis. Int J Innov Comp Inf Control 8:747–761Google Scholar
  16. Chin KS, Wang YM, Poon GKK, Yang JB (2009) Failure mode and effects analysis using a group-based evidential reasoning approach. Comput Oper Res 36:1768–1779CrossRefzbMATHGoogle Scholar
  17. Delgado M, Herrera F, Herrera-Viedma E, Martin-Bautista MJ, Martinez L, Vila MA (2002) A communication model based on the 2-tuple fuzzy linguistic representation for a distributed intelligent agent system on internet. Soft Comput 6:320–328CrossRefzbMATHGoogle Scholar
  18. Ford Motor Company (1988) Potential failure mode and effects analysis (FMEA) reference manualGoogle Scholar
  19. Gabus A, Fontela E (1973) Perceptions of the world problematique: communication procedure, communicating with those bearing collective responsibility (DEMATEL report no. 1). Battelle Geneva Research Centre, Geneva, SwitzerlandGoogle Scholar
  20. Guimaraes ACF, Lapa CMF (2007) Fuzzy inference to risk assessment on nuclear engineering systems. Appl Soft Comput 7:17–28CrossRefGoogle Scholar
  21. Herrera F, Martinez L (2000) A 2-tuple fuzzy linguistic representation model for computing with words. IEEE T Fuzzy Syst 8:746–752CrossRefMathSciNetGoogle Scholar
  22. Jiang Y, Tang Y, Chen Q, Liu H, Tang J (2010) Interval-valued intuitionistic fuzzy soft sets and their properties. Comput Math Appl 60:906–918CrossRefzbMATHMathSciNetGoogle Scholar
  23. Jun YB (2008) Soft BCK/BCI-algebras. Comput Math Appl 56:1408–1413CrossRefzbMATHMathSciNetGoogle Scholar
  24. Jun YB, Park CH (2008) Applications of soft sets in ideal theory of BCK/BCI-algebras. Inform Sci 178:2466–2475zbMATHMathSciNetGoogle Scholar
  25. Lai PT (2011) A novel approach to modify the conventional risk priority number of FMEA. National Chiao Tung University, TaiwanGoogle Scholar
  26. Lin CJ, Wu WW (2008) A casual analytical method for group decision-making under fuzzy environment. Expert Syst Appl 34:205–213CrossRefGoogle Scholar
  27. Maji PK, Roy AR, Biswas R (2002) An application of soft sets in a decision making problem. Comput Math Appl 44:1077–1083CrossRefzbMATHMathSciNetGoogle Scholar
  28. Maji PK, Biswas R, Roy AR (2003) Soft set theory. Comput Math Appl 45:555–562CrossRefzbMATHMathSciNetGoogle Scholar
  29. Majumdar P, Samanta SK (2010) Generalised fuzzy soft sets. Comput Math Appl 59:1425–1432CrossRefzbMATHMathSciNetGoogle Scholar
  30. Molodtsov D (1999) Soft set theory-first results. Comput Math Appl 37:19–31CrossRefzbMATHMathSciNetGoogle Scholar
  31. Pillay A, Wang J (2003) Modified failure mode and effects analysis using approximate reasoning. Reliab Eng Syst Saf 79:69–85CrossRefGoogle Scholar
  32. Roy AR, Maji PK (2007) A fuzzy soft set theoretic approach to decision making problems. J Comput Appl Math 203:412–418CrossRefzbMATHGoogle Scholar
  33. Sankar NR, Prabhu BS (2001) Modified approach for prioritization of failures in a system failure mode and effects analysis. Int J Qual Reliab Manage 18:324–335CrossRefGoogle Scholar
  34. 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 Saf 91:872–881CrossRefGoogle Scholar
  35. Sharma RK, Kumar D, Kumar P (2005) Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modeling. Int J Qual Reliab Manage 22:986–1004CrossRefGoogle Scholar
  36. Sharma RK, Kumar D, Kumar P (2008) Fuzzy modeling of system behavior for risk and reliability analysis. Int J Syst Sci 39:563–581CrossRefMathSciNetGoogle Scholar
  37. Teoh PC, Case K (2005) An evaluation of failure modes and effects analysis generation method for conceptual design. Int J Comput Integ M 18:279–293CrossRefGoogle Scholar
  38. US Department of Defense Washington, DC (1980) Procedures for performing a failure mode effects and criticality analysis, US MIL-STD-1629AGoogle Scholar
  39. Wang J, Ruxton T, Labrie CR (1995) Design for safety of engineering systems with multiple failure state variables. Reliab Eng Syst Saf 50:271–284CrossRefGoogle Scholar
  40. Xu K, Tang LC, Xie M, Ho SL, Zhu ML (2002) Fuzzy assessment of FMEA for engine systems. Reliab Eng Syst Saf 75:17–29CrossRefGoogle Scholar
  41. Zou Y, Xiao Z (2008) Data analysis approaches of soft sets under incomplete information. Knowl-Based Syst 21:941–945CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Management SciencesR.O.C. Military AcademyKaohsiungTaiwan

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