Skip to main content
SpringerLink
Log in

Developing the Neutrosophic Fuzzy FMEA Method as Evaluating Risk Assessment Tool

  • Conference paper
  • First Online:
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making (INFUS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1029))

Included in the following conference series:

Abstract

Failure mode and effects analysis (FMEA) is one of the most effective technique in the field of risk management and is generally preferred to improve process reliability in manufacturing and service sector. Determining the risk priority numbers (RNP) of potential failures is of prime importance for the success of implementing FMEA. The parameters of RNP are generally achieved from experiences and engineering decisions which include uncertainty and vagueness because of the linguistic structure. Fuzzy set theory is preferable approach in order to handle these vagueness in determining the RNP. In this study, single valued neutrosophic Fuzzy FMEA has been firstly developed and it is applied to improve the overhaul process of a fighter jet engine turbine. The proposed model has been tested in an application that analyzes failure modes for maintenance of Low Pressure Turbin (LPT) Rotor Shaft.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Stamatis, D.H.: Failure Mode and Effect Analysis: FMEA From Theory to Execution. ASQ Quality Press, Milwaukee (1995)

    Google Scholar 

  2. Xu, K., Tang, L.C., Xie, M., Ho, S.L., Zhu, M.L.: Fuzzy assessment of FMEA for engine systems. Reliab. Eng. Syst. Saf. 75(1), 17–29 (2002)

    Article  Google Scholar 

  3. Guimaraes, A.C.F., Lapa, C.M.F.: Fuzzy FMEA applied to PWR chemical and volume control system. Prog. Nucl. Energy 44(3), 191–213 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Keskin, G.A., Özkan, C.: An alternative evaluation of FMEA: fuzzy ART algorithm. Qual. Reliab. Eng. Int. 25(6), 647–661 (2009)

    Article  Google Scholar 

  7. Chin, K.-S., Chan, A., Yang, J.-B.: Development of a fuzzy FMEA based product design system. Int. J. Adv. Manuf. Technol. 36(7/8), 633–649 (2008)

    Article  Google Scholar 

  8. Abdelgawad, M., Fayek, A.: Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP. J. Constr. Eng. Manag. 136(9), 1028–1036 (2011)

    Article  Google Scholar 

  9. Hassan, A., Siadat, A., Dantan, J.-Y., Martin, P.: Conceptual process planning–an improvement approach using QFD, FMEA, and ABC methods. Robot. Comput.-Integr. Manuf. 26(4), 392–400 (2010)

    Article  Google Scholar 

  10. Braglia, M., Frosolini, M., Montanari, R.: Fuzzy criticality assessment model for failure modes and effects analysis. Int. J. Qual. Reliab. Manag. 20(4), 503–524 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Garcia, P.A.A., Schirru, R., Melo, P.F.F.: A fuzzy data envelopment analysis approach for FMEA. Prog. Nucl. Energy 46(3–4), 359–373 (2005)

    Article  Google Scholar 

  13. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

  14. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986)

    Article  Google Scholar 

  15. Smarandache, F.: A Unifying Field in Logics Neutrosophy: Neutrosophic Probability, Set and Logic. American Research Press, Rehoboth (1999)

    MATH  Google Scholar 

  16. Biswas, P., Pramanik, S., Giri, B.: TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Comput. Appl. 27, 727–737 (2016)

    Article  Google Scholar 

  17. Wang, H., Smarandache, F., Zhang, Y.Q., Sunderraman, R.: Single valued neutrosophic sets. Multispace Multistruct. 4, 410–413 (2010)

    MATH  Google Scholar 

  18. Gallego Lupiáñez, F.: Interval neutrosophic sets and topology. Kybernetes 38(3/4), 621–624 (2009)

    Article  Google Scholar 

  19. Hezam, I.M., Abdel-Baset, M., Smarandache, F.: Taylor series approximation to solve neutrosophic multiobjective programming problem. Neutrosophic Sets Syst. 10, 39–46 (2018)

    Google Scholar 

  20. El-Hefenawy, N., Metwally, M.A., Ahmed, Z.M., El-Henawy, I.M.: A review on the applications of neutrosophic sets. J. Comput. Theor. Nanosci. 13, 936–944 (2016)

    Article  Google Scholar 

  21. Deli, I., Subas, Y.: Single valued neutrosophic numbers and their applications to multicriteria decision making problem. Neutrosophic Sets Syst. 2, 1–13 (2014)

    Google Scholar 

  22. Abdel-Baset, M., Hezam, I.M., Smarandache, F.: Neutrosophic goal programming. Neutrosophic Sets Syst. 11, 112–118 (2016)

    Google Scholar 

  23. Abdel-Baseta, M., Changb, V., Gamala, A., Smarandachec, F.: An integrated neutrosophic ANP and VIKOR method for achieving sustainable supplier selection: a case study in importing field. Comput. Ind. 106, 94–110 (2019)

    Article  Google Scholar 

  24. Abdel, S.I., Abd Ellatif, A., Hassan, M.M.: Two ranking methods of single valued triangular neutrosophic numbers to rank and evaluate information systems quality. Neutrosophic Sets Syst. 19, 132 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Eskisehir Technical University, Eskişehir, Turkey

    Sezen Ayber & Nihal Erginel

Authors
  1. Sezen Ayber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nihal Erginel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sezen Ayber .

Editor information

Editors and Affiliations

  1. Management Faculty, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Industrial Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  3. Management Faculty, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  4. Management Faculty, Istanbul Technical University, İstanbul, Turkey

    Basar Oztaysi

  5. Industrial Engineering Department, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

  6. Management Faculty, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ayber, S., Erginel, N. (2020). Developing the Neutrosophic Fuzzy FMEA Method as Evaluating Risk Assessment Tool. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_133

Download citation

Publish with us

Policies and ethics

Search

Navigation