Skip to main content
Log in

Failure Mode and Effects Analysis (FMEA) for Traffic Risk Assessment Based on Unbalanced Double Hierarchy Linguistic Term Set

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

As an important technique in safety and reliability analysis, failure mode and effects analysis (FMEA) has been widely utilized to identify and eliminate existing and potential failure. When evaluating the failure modes (FMs), the weights of different FMs are always incompletely known, and different information forms are used to describe the risk assessment issues. The objective of the paper is developing a new approach to solve risk assessment problems faced by four transportation forms in fuzzy and complex environment. In the process of the assessment, the unbalanced double hierarchy linguistic term set (UDHLTS) is used as a linguistic technique to describe the information reasonably and practically. The contributions of this paper are reflected in the following four aspects. Firstly, three linguistic scale functions (LSFs) of the UDHLTS are improved and a semantic model with distinct linguistic cognitive bias parameters is constructed and unified. Secondly, multi-attribute decision-making (MADM) method is applied to FMEA, and an FMEA-MACBETH model is presented to address the risk assessment problems under UDHLTS environment. Meanwhile, weight determination method is built based on the CRITIC method under the situation of completely unknown weights. Finally, a case study of risk assessment in different transportation forms is used to explain the feasibility and rationality of the presented method. Comparison and discussion are conducted to further demonstrate the advantages of the proposed method.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Khan, R.U., Yin, J., Mustafa, F.S., Liu, H.: Risk assessment and decision support for sustainable traffic safety in Hong Kong waters. IEEE Access 8, 72893–72909 (2020)

    Google Scholar 

  2. Ghoushchi, S.J., Yousefi, S., Khazaeili, M.: An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Appl. Soft Comput. 81, 105505 (2019)

    Google Scholar 

  3. Qin, J., Yan, X., Pedrycz, W.: Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method. Appl. Soft Comput. 89, 106134 (2020)

    Google Scholar 

  4. Wang, W., Liu, X., Chen, X., Qin, Y.: Risk assessment based on hybrid FMEA framework by considering decision maker’s psychological behavior character. Comput. Ind. Eng. 136, 516–527 (2019)

    Google Scholar 

  5. Certa, A., Hopps, F., Inghilleri, R., La Fata, C.M.: A Dempster-Shafer theory-based approach to the failure mode, effects and criticality analysis (FMECA) under epistemic uncertainty: application to the propulsion system of a fishing vessel. Reliab. Eng. Syst. Saf. 159, 69–79 (2017)

    Google Scholar 

  6. Tsai, S.B., Yu, J., Ma, L., Luo, F., Zhou, J., Chen, Q., Xu, L.: A study on solving the production process problems of the photovoltaic cell industry. Renew. Sustain. Energy Rev. 82, 3546–3553 (2018)

    Google Scholar 

  7. Wang, W., Liu, X., Qin, Y., Fu, Y.: A risk evaluation and prioritization method for FMEA with prospect theory and Choquet integral. Saf. Sci. 110, 152–163 (2018)

    Google Scholar 

  8. Bhattacharjee, P., Dey, V., Mandal, U.K.: Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model. Saf. Sci. 132, 104967 (2020)

    Google Scholar 

  9. Bian, T., Zheng, H., Yin, L., Deng, Y.: Failure mode and effects analysis based on D numbers and TOPSIS. Qual. Reliab. Eng. Int. 34(4), 501–515 (2018)

    Google Scholar 

  10. Wu, J.Y., Hsiao, H.I.: Food quality and safety risk diagnosis in the food cold chain through failure mode and effect analysis. Food Control 120, 107501 (2021)

    Google Scholar 

  11. Liu, P., Shen, M.J.: An extended C-TODIM method with linguistic intuitionistic fuzzy numbers. J. Intell. Fuzzy Syst. 37(3), 3615–3627 (2019)

    Google Scholar 

  12. Liu, P., Shen, M., Teng, F., Zhu, B., Rong, L., Geng, Y.: Double hierarchy hesitant fuzzy linguistic entropy-based Todim approach using evidential theory. Inform. Sci. 547(8), 223–243 (2020)

    Google Scholar 

  13. Liu, P., Wang, P.: Multiple-attribute decision-making based on Archimedean Bonferroni operators of q-rung orthopair fuzzy numbers. IEEE Trans. Fuzzy Syst. 27(5), 834–848 (2018)

    Google Scholar 

  14. Liu, P., Zhu, B., Wang, P., Shen, M.: An approach based on linguistic spherical fuzzy sets for public evaluation of shared bicycles in China. Eng. Appl. Artif. Intell. 87, 103295 (2020)

    Google Scholar 

  15. Liu, H.C., Li, Z., Song, W., Su, Q.: Failure mode and effect analysis using cloud model theory and PROMETHEE method. IEEE Trans. Reliab. 66(4), 1058–1072 (2017)

    Google Scholar 

  16. Liu, H.C., Wang, L.N., Li, Z., Hu, Y.P.: Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. IEEE Trans. Fuzzy Syst. 27, 84–95 (2019)

    Google Scholar 

  17. Liu, H.C., You, J.X., Li, P., Su, Q.: Failure mode and effect analysis under uncertainty: an integrated multiple criteria decision making approach. IEEE Trans. Reliab. 65(3), 1380–1392 (2016)

    Google Scholar 

  18. Ait-Mlouk, A., Gharnati, F., Agouti, T.: An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety. Eur. Transp. Res. Rev. 9(3), 40 (2017)

    Google Scholar 

  19. Akyuz, G., Tosun, O., Aka, S.: Multi criteria decision-making approach for evaluation of supplier performance with MACBETH method. Int. J. Inform. Decis. Sci. 10(3), 249–262 (2018)

    Google Scholar 

  20. Arun, A., Haque, M.M., Bhaskar, A., Washington, S., Sayed, T.: A systematic mapping review of surrogate safety assessment using traffic conflict techniques. Accid. Anal. Prev. 153, 106016 (2021)

    Google Scholar 

  21. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    MATH  Google Scholar 

  22. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 8(3), 199–249 (1975)

    MathSciNet  MATH  Google Scholar 

  23. Gou, X., Liao, H., Xu, Z., Herrera, F.: Double hierarchy hesitant fuzzy linguistic MULTIMOORA method for evaluating the implementation status of haze controlling measures. Inform. Fusion 38, 22–34 (2017)

    Google Scholar 

  24. Gou, X., Xu, Z., Liao, H., Herrera, F.: Multiple criteria decision making based on distance and similarity measures under double hierarchy hesitant fuzzy linguistic environment. Comput. Ind. Eng. 126, 516–530 (2018)

    Google Scholar 

  25. Gou, X., Xu, Z.: Double hierarchy linguistic term set and its extensions: the state-of-the-art survey. Int. J. Intell. Syst. 36(2), 832–865 (2021)

    Google Scholar 

  26. Gou, X., Xu, Z., Herrera, F.: Consensus reaching process for large-scale group decision making with double hierarchy hesitant fuzzy linguistic preference relations. Knowl.-Based Syst. 157, 20–33 (2018)

    Google Scholar 

  27. Krishankumar, R., Ravichandran, K.S., Shyam, V., Sneha, S.V., Garg, H.: Multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic preference information. Neural Comput. Appl. 17, 14031–14045 (2020)

    Google Scholar 

  28. Montserrat-Adell, J., Xu, Z., Gou, X., Agell, N.: Free double hierarchy hesitant fuzzy linguistic term sets: an application on ranking alternatives in GDM. Inform. Fusion 47, 45–59 (2019)

    Google Scholar 

  29. Zhou, W., Xu, Z.: Generalized asymmetric linguistic term set and its application to qualitative decision making involving risk appetites. Eur. J. Operat. Res. 254, 610–621 (2016)

    MathSciNet  MATH  Google Scholar 

  30. Wang, J., Wang, J.Q., Zhang, H.Y.: A likelihood-based TODIM approach based on multi-hesitant fuzzy linguistic information for evaluation in logistics outsourcing. Comput. Ind. Eng. 99, 287–299 (2016)

    Google Scholar 

  31. Liao, H.C., Qin, R., Gao, C.Y., Wu, X.L., Hafezalkotob, A., Herrera, F.: Score-HeDLiSF, a score function of hesitant fuzzy linguistic term set based on hesitant degrees and LSFs: an application to unbalanced hesitant fuzzy linguistic MULTIMOORA. Inform. Fusion 48, 39–54 (2019)

    Google Scholar 

  32. Fu, Z., Liao, H.: Unbalanced double hierarchy linguistic term set: the TOPSIS method for multi-expert qualitative decision making involving green mine selection. Inform. Fusion 51, 271–286 (2019)

    Google Scholar 

  33. Bana e Costa, C.A., Chagas, M.P.: A career choice problem: an example of how to use MACBETH to build a quantitative value model based on qualitative value judgments. Eur. J. Operat. Res. 153(2), 323–331 (2004)

    MATH  Google Scholar 

  34. Liu, H.C., Chen, X.Q., Duan, C.Y., Wang, Y.M.: Failure mode and effect analysis using multi-criteria decision making methods: a systematic literature review. Comput. Ind. Eng. 135, 881–897 (2019)

    Google Scholar 

  35. Huang, J., You, J.X., Liu, H.C., Song, M.S.: Failure mode and effect analysis improvement: a systematic literature review and future research agenda. Reliab. Eng. Syst. Saf. 199, 106885 (2020)

    Google Scholar 

  36. Aboutorab, H., Saberi, M., Asadabadi, M.R., Hussain, O., Chang, E.: ZBWM: the Z-number extension of best worst method and its application for supplier development. Expert Syst. Appl. 107, 115–125 (2018)

    Google Scholar 

  37. Falak, N., Rajabi, A.M., Khalid, J.N., Khadeer, H.O., Elizabeth, C., Morteza, S.: An MCDM method for cloud service selection using a Markov chain and the best-worst method. Knowl.-Based Syst. 159, 120–131 (2018)

    Google Scholar 

  38. Hashemkhani, Z.S., Bahrami, M.: Investment prioritizing in high tech industries based on SWARA-COPRAS approach. Technol. Econ. Dev. Econ. 20(3), 534–553 (2014)

    Google Scholar 

  39. Liu, P., Tang, G.: Some intuitionistic fuzzy prioritized interactive Einstein Choquet operators and their application in decision making. IEEE Access 6, 72357–72371 (2019)

    Google Scholar 

  40. Diakoulaki, D., Mavrotas, G., Papayannakis, L.: Determining objective weights in multiple criteria problems: the critic method. Comput. Oper. Res. 22(7), 763–770 (1995)

    MATH  Google Scholar 

  41. Krishnan, A.R., Kasim, M.M., Hamid, R., Ghazali, M.F.: A modified CRITIC method to estimate the objective weights of decision criteria. Symmetry 13(6), 973 (2021)

    Google Scholar 

  42. Joshi, R., Satish, K.: A novel fuzzy decision-making method using entropy weights-based correlation coefficients under intuitionistic fuzzy environment. Int. J. Fuzzy Syst. 21(1), 232–242 (2019)

    MathSciNet  Google Scholar 

  43. Zavadskas, E.K., Podvezko, V.: Integrated determination of objective criteria weights in MCDM. Int. J. Inf. Technol. Decis. Mak. 15, 267–283 (2016)

    Google Scholar 

  44. Li, Y., Wang, R., Chin, K.: New failure mode and effect analysis approach considering consensus under interval-valued intuitionistic fuzzy environment. Soft. Comput. 23, 11611–11626 (2019)

    Google Scholar 

  45. Nunes, L.C., Pinheiro, P.R., Pinheiro, M.C.D., Simao, M., Nunes, R.E.C.: Toward a novel method to support decision-making process in health and behavioral factors analysis for the composition of IT projects teams. Neural Comput. App. 32(15), 11019–11040 (2020)

    Google Scholar 

  46. Zhang, Z., Yang, L., Cao, Y., et al.: An improved FMEA method based on ANP with probabilistic linguistic term sets. Int. J. Fuzzy Syst. 24, 1–26 (2022). https://doi.org/10.1007/s40815-022-01302-2

    Article  Google Scholar 

  47. Bowles, J.B., Peláez, C.E.: Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliab. Eng. Syst. Saf. 50(2), 203–213 (1995)

    Google Scholar 

  48. Teng, F., Liu, P., Zhang, L., Zhao, J.: Multiple attribute decision-making methods with unbalanced linguistic variables based on Maclaurin symmetric mean operators. Int. J. Inform. Technol. Decis. Making (IJITDM) 18(01), 105–146 (2019)

    Google Scholar 

Download references

Acknowledgment

This paper is supported by the National Natural Science Foundation of China (No. 71771140), the Taishan Scholars Project of Shandong Province, Shandong Provincial Key Research and Development Program(Major Scientific and Technological Innovation Project) (Nos. 2021SFGC0102, 2020CXGC010110).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peide Liu.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, P., Shen, M. Failure Mode and Effects Analysis (FMEA) for Traffic Risk Assessment Based on Unbalanced Double Hierarchy Linguistic Term Set. Int. J. Fuzzy Syst. 25, 423–450 (2023). https://doi.org/10.1007/s40815-022-01412-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-022-01412-x

Keywords

Navigation