Advertisement

Journal of Intelligent Manufacturing

, Volume 25, Issue 6, pp 1413–1427 | Cite as

Applying the concept of exponential approach to enhance the assessment capability of FMEA

  • Kuei-Hu Chang
  • Yung-Chia Chang
  • Pei-Ting Lai
Article

Abstract

Failure modes and effects analysis (FMEA) has been used to identify the critical risk events and predict a system failure to avoid or reduce the potential failure modes and their effect on operations. The risk priority number (RPN) is the classical method to evaluate the risk of failure in conventional FMEA. RPN, which ranges from 1 to 1000, is a mathematical product of three parameters—severity (\(S\)), occurrence (\(O\)), and detection (\(D\))—to rank and assess the risk of potential failure modes. However, there are some shortcomings of the conventional RPN method, such as: the RPN elements have many duplicate numbers; violate the assumption of measurement scales; and have not considered the weight of \(S, O\), and \(D\). In order to improve the aforementioned shortcomings of the conventional RPN calculation problem, this paper presents an easy yet effective method to enhance the risk evaluation capability of FMEA. The new method is named exponential risk priority number (ERPN), which uses a simple addition function to the exponential form of \(S, O\), and \(D\) to substitute the conventional RPN method, which is a mathematical product of three parameters. Two practical cases are used to demonstrate that the ERPN method can not only resolve some problems of the conventional RPN method but also is able to provide a more accurate and reasonable risk assessment in FMEA.

Keywords

Risk assessment Failure modes and effects analysis  Risk priority number Exponential risk priority number Data envelopment analysis 

Notes

Acknowledgments

The author would like to express his sincerest gratitude to 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. Automotive Industry Action Group (AIAG). (2008). Potential failure mode and effect analysis (FMEA) reference manual. FMEA reference manual (4th ed).Google Scholar
  2. Ben-Daya, M., & Raouf, A. (1996). A revised failure mode and effects analysis model. International Journal of Quality and Reliability Management, 13(1), 43–47.CrossRefGoogle Scholar
  3. Bowles, J. B. (1998). The new SAE FMECA standard. In Processing annual reliability and maintainability, symposium, pp. 48–53.Google Scholar
  4. Bowles, J. B. (2003). An assessment of RPN prioritization in a failure modes effects and criticality analysis. In Processing annual reliability and maintainability, symposium, pp. 380–386.Google Scholar
  5. Bowles, J. B., & Pelaez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure modes, effects and criticality analysis. Reliability Engineering and System Safety, 50(2), 203–213.CrossRefGoogle Scholar
  6. Braglia, M., Frosolini, M., & Montanari, R. (2003). Fuzzy TOPSIS approach for failure mode, effects and criticality analysis. Quality and Reliability Engineering International, 19(5), 425–443.CrossRefGoogle Scholar
  7. Chang, C. L., Wei, C. C., & Lee, Y. H. (1999). Failure mode and effects analysis using fuzzy method and grey theory. Kybernetes, 28(3–7), 1072–1080.CrossRefGoogle Scholar
  8. Chang, D. S., & Sun, K. L. P. (2009). Applying DEA to enhance assessment capability of FMEA. International Journal of Quality and Reliability Management, 26(6), 629–643.Google Scholar
  9. Chang, K. H. (2009). Evaluate the orderings of risk for failure problems using a more general RPN methodology. Microelectronics Reliability, 49(12), 1586–1596.CrossRefGoogle Scholar
  10. Chang, K. H., & Cheng, C. H. (2009). A novel general approach to evaluating the PCBA reliability for components with different membership function. Applied Soft Computing, 9(3), 1044–1056.CrossRefGoogle Scholar
  11. Chang, K. H., & Cheng, C. H. (2010). A risk assessment methodology using intuitionistic fuzzy set in FMEA. International Journal of Systems Science, 41(12), 1457–1471.CrossRefGoogle Scholar
  12. Chang, K. H., & Cheng, C. H. (2011). Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. Journal of Intelligent Manufacturing, 22(2), 113–129.CrossRefGoogle Scholar
  13. Chang, K. H., Cheng, C. H., & Chang, Y. C. (2010). Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique. Soft Computing, 14(3), 285–298.CrossRefGoogle Scholar
  14. Chien, C. F., & Zheng, J. N. (2012). Mini-max regret strategy for robust capacity expansion decisions in semiconductor manufacturing. Journal of Intelligent Manufacturing, 23(6), 2151–2159.CrossRefGoogle Scholar
  15. Chin, K. S., Wang, Y. M., Poon, G. K. K., & Yang, J. B. (2009). Failure mode and effects analysis by data envelopment analysis. Decision Support Systems, 48(1), 246–256.CrossRefGoogle Scholar
  16. Ford Motor Company. (1988). Potential failure mode and effects analysis. Instruction manual.Google Scholar
  17. Gabus, A., & Fontela, E. (1973). Perception of the world problematique: Communication procedure, communicating with those bearing collective responsibility (DEMATEL report no.1). Geneva, Switzerland: Battelle Geneva Research Centre.Google Scholar
  18. Gilchrist, W. (1993). Modelling failure modes and effects analysis. International Journal of Quality and Reliability Management, 10(5), 16–23.CrossRefGoogle Scholar
  19. Hsiao, T. Y., & Lu, C. N. (2008). Risk informed design refinement of a power system protection scheme. IEEE Transactions on Reliability, 57(2), 311–321.CrossRefGoogle Scholar
  20. Hussain, O. K., Dillon, T., Hussain, F. K., & Chang, E. (2012). Probabilistic assessment of loss in revenue generation in demand-driven production. Journal of Intelligent Manufacturing, 23(6), 2069–2084.Google Scholar
  21. International Electrotechnical Commission, Geneva. (1985). Analysis techniques for system reliability-procedures for failure mode and effect, analysis. IEC812.Google Scholar
  22. Karlsson, B., Karlsson, N., & Wide, P. (2000). A dynamic safety system based on sensor fusion. Journal of Intelligent Manufacturing, 11(5), 475–483.CrossRefGoogle Scholar
  23. Kubat, C., & Yuce, B. (2012). A hybrid intelligent approach for supply chain management system. Journal of Intelligent Manufacturing, 23(4), 1237–1244.CrossRefGoogle Scholar
  24. Pillay, A., & Wang, J. (2003). Modified failure mode and effects analysis using approximate reasoning. Reliability Engineering and System Safety, 79(1), 69–85.CrossRefGoogle Scholar
  25. Sankar, N. R., & Prabhu, B. S. (2001). Modified approach for prioritization of failures in a system failure mode and effects analysis. International Journal of Quality and Reliability Management, 18(3), 324–335.CrossRefGoogle Scholar
  26. Seyed-Hosseini, S. M., Safaei, N., & Asgharpour, M. J. (2006). Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique. Reliability Engineering and System Safety, 91(8), 872–881.CrossRefGoogle Scholar
  27. Sharma, R. K., Kumar, D., & Kumar, P. (2005). Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modeling. International Journal of Quality and Reliability Management, 22(9), 986–1004.Google Scholar
  28. Tay, K. M., & Lim, C. P. (2006). Application of fuzzy inference techniques to FMEA. Applied Soft Computing Technologies: The Challenge of Complexity, 34, 161–171.CrossRefGoogle Scholar
  29. US Department of Defense Washington, DC (1974). Procedures for performing a failure mode effects and criticality analysis. US MIL-STD-1629.Google Scholar
  30. US Department of Defense Washington, DC (1980). Procedures for performing a failure mode effects and criticality analysis. US MIL-STD-1629A.Google Scholar
  31. Wang, J., Ruxton, T., & Labrie, C. R. (1995). Design for safety of engineering systems with multiple failure state variables. Reliability Engineering and System Safety, 50(3), 271–284. Google Scholar
  32. Wang, Y. M., Chin, K. S., Poon, G. K. K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert Systems with Applications, 36, 1195–1207.CrossRefGoogle Scholar
  33. Xu, K., Tang, L. C., Xie, M., Ho, S. L., & Zhu, M. L. (2002). Fuzzy assessment of FMEA for engine system. Reliability Engineering and System Safety, 75(1), 17–29.CrossRefGoogle Scholar
  34. Yeh, R. H., & Hsieh, M. H. (2007). Fuzzy assessment of FMEA for a sewage plant. Journal of the Chinese Institute of Industrial Engineers, 24(6), 505–512.CrossRefGoogle Scholar
  35. Zhang, D. Y., Cao, X., Wang, L., & Zeng, Y. (2012). Mitigating the risk of information leakage in a two-level supply chain through optimal supplier selection. Journal of Intelligent Manufacturing, 23(4), 1351–1364.CrossRefGoogle Scholar
  36. Zhu, J. (2003). Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets and DEA Excel Solver. Boston: Kluwer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Management SciencesR.O.C. Military AcademyKaohsiungTaiwan
  2. 2.Department of Industrial Engineering and ManagementNational Chiao Tung UniversityHsinchuTaiwan

Personalised recommendations