Assessment of structural risks using the fuzzy weighted Euclidean FMEA and block diagram analysis

  • Jihyun Park
  • Changsoon Park
  • Suneung Ahn


Failure mode and effects analysis (FMEA) is a risk assessment method in products, processes, or systems for appropriate corrective actions. Although FMEA techniques are used in various industries, it has been criticized for several shortcomings. First, conventional FMEA entirely depends on qualitative evaluation. Second, traditional FMEA does not consider the functional influence between components of a system, meaning that it cannot be applied to systems-complicated influence relationships. Third, risk priority number (RPN) in traditional FMEA, which is evaluated in crisp values of severity (S), occurrence rate (O), and the probability of not detecting the failure (D), can lead to a RPN distortion problem in which contradictory interpretations of RPN result from, respectively, reasonable risk factors. In order to overcome these shortcomings, this paper proposes a new risk assessment method using importance risk priority number (IRPN). The IRPN, which is composed of structural and relational importance, is attained by taking a three-dimensional geometric approach to fuzzy weighted Euclidean (FWE) FMEA and risk block diagram analysis. The proposed risk assessment method is applied in the numerical example and empirical example of the thin film transistor liquid crystal display (TFT-LCD) products. Comparison results with previous FMEA methods show that the proposed method not only overcomes the shortcomings of previous FMEA methods, such as the RPN distortion, but is also useful for assessing the structural risks that involve functional influence between risks. In addition, a three-dimensional approach based on fuzzy logic is more analytical and applicable than previous methods.


Failure mode and effect analysis Fuzzy weighted Euclidean Block diagram analysis Relational importance Structural importance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2016R1A2B1008163).


  1. 1.
    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):203–213CrossRefGoogle Scholar
  2. 2.
    US Department of Defense (1980) Procedures for performing a failure mode, effects and criticality analysis. MIL-STD-1629. Washington (DC)Google Scholar
  3. 3.
    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):357–368CrossRefGoogle Scholar
  4. 4.
    Liu H-C, Liu L, Liu N (2013) Risk evaluation approaches in failure mode and effects analysis: a literature review. Expert Syst Appl 40(2):828–838CrossRefGoogle Scholar
  5. 5.
    Wang J, Ruxton T, Labrie C (1995) Design for safety of engineering systems with multiple failure state variables. Reliab Eng Syst Saf 50(3):271–284CrossRefGoogle Scholar
  6. 6.
    Xu K, Tang LC, Xie M, Ho S, Zhu M (2002) Fuzzy assessment of FMEA for engine systems. Reliab Eng Syst Saf 75(1):17–29CrossRefGoogle Scholar
  7. 7.
    Wang Y-M, Chin K-S, Poon GKK, Yang J-B (2009) Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert Syst Appl 36(2):1195–1207CrossRefGoogle Scholar
  8. 8.
    Liu H-C, Chen Y-Z, You J-X, Li H (2014) Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach. J Intell Manuf:1–12Google Scholar
  9. 9.
    Chang K-H (2009) Evaluate the orderings of risk for failure problems using a more general RPN methodology. Microelectron Reliab 49(12):1586–1596CrossRefGoogle Scholar
  10. 10.
    Chang K-H, Cheng C-H (2011) Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. J Intell Manuf 22(2):113–129CrossRefGoogle Scholar
  11. 11.
    Liu H-C, You J-X, Lin Q-L, Li H (2015) Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory. Int J Comput Integr Manuf 28(7):701–714CrossRefGoogle Scholar
  12. 12.
    Seyed-Hosseini S, Safaei N, Asgharpour M (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(8):872–881CrossRefGoogle Scholar
  13. 13.
    Liou T-S, Wang M-JJ (1992) Fuzzy weighted average: an improved algorithm. Fuzzy Sets Syst 49(3):307–315MathSciNetCrossRefGoogle Scholar
  14. 14.
    Lee W-K (2006) Risk assessment modeling in aviation safety management. J Air Transp Manag 12(5):267–273CrossRefGoogle Scholar
  15. 15.
    Van Broekhoven E, De Baets B A comparison of three methods for computing the center of gravity defuzzification. In: Fuzzy Systems, 2004. Proceedings. 2004 I.E. International Conference on, 2004. IEEE, p 1537–1542Google Scholar
  16. 16.
    Xiao N, Huang H-Z, Li Y, He L, Jin T (2011) Multiple failure modes analysis and weighted risk priority number evaluation in FMEA. Eng Fail Anal 18(4):1162–1170CrossRefGoogle Scholar
  17. 17.
    Pickard K, Müller P, Bertsche B Multiple failure mode and effects analysis-an approach to risk assessment of multiple failures with FMEA. In: Reliability and Maintainability Symposium, 2005. Proceedings. Annual, 2005. IEEE, p 457–462Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial and Management EngineeringHanyang UniversitySeoulSouth Korea
  2. 2.Department of Mechanical EngineeringHanyang UniversityAnsanSouth Korea
  3. 3.Department of Industrial and Management EngineeringHanyang UniversityAnsanSouth Korea

Personalised recommendations