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Integrated Fuzzy MCDM Frameworks in Risk Prioritization of Failure Modes

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Frontiers of Performability Engineering

Part of the book series: Risk, Reliability and Safety Engineering ((RRSE))

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Abstract

Failure Modes and Effects and Criticality Analysis (FMECA) is a widely adopted proactive risk assessment and prioritization approach among the reliability and safety engineers. According to AS/NZS IEC 60812:2020, the associated risk of a failure mode is expressed in terms of a risk priority number (RPN)—a product of three risk factors. However, in case of unavailability and/or inability to obtain the field data, the risk factors are often subjectively assessed by a team of cross-functional experts, which are later translated into crisp values by employing predefined and customized scales on risk factors. These subjective and/or crisp evaluations contain some inherent uncertainties, that can negatively impact on proper risk ordering of failure modes. To overcome some of the limitations of the traditional approach, this chapter describes two novel integrated multi-criteria decision-making (MCDM) frameworks by employing the concepts of type-1 fuzzy sets/fuzzy sets and interval type-2 fuzzy sets (IT2FSs). The IT2F-decision-making trial and evaluation laboratory (IT2F-DEAMTEL) method is adopted to calculate the weights as well as causal dependencies among the risk factors. These weights are further utilized in two MCDM methods (viz., modified multi-attributive ideal real comparative analysis (fuzzy MAIRCA), and modified fuzzy measurement of alternatives and ranking according to compromise solution (fuzzy MARCOS)), which are solely aimed at risk ranking of failure modes. To validate the ranking abilities of the integrated frameworks, a FMECA case study on process plant gearbox is considered. Finally, the validations of the obtained ranking results are performed by comparing the outputs with other popular fuzzy MCDM methods and through detailed sensitivity analyses.

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Notes

  1. 1.

    In Pamucar et al. [34], the computed chances of selection of any failure mode are crisp in nature. For the sake of calculation, we convert it into a TFN. For example, assume that there are eight failure modes in a FMECA case study. Then their chances of selection are computed as: \({P}_{\text{FM}_{i}}=\frac{1}{8}=0.125\). This outcome can also be represented as a TFN: \(\left(\mathrm{0.125,0.125,0.125}\right)\), whose defuzzification yields the value of 0.125. Also, Pamucar et al. [34], employed min–max normalization.

  2. 2.

    One can also refer and adopt the approach by Chatterjee et al. [13], Pamučar et al. [33].

  3. 3.

    In Boral et al. [6, 7], concluded that both modified fuzzy MARCOS and modified fuzzy MAIRCA methods have same ranking stability while solving the FMECA case study of Kutlu and Ekmekçioğlu [23].

References

  1. Akbari R, Dabbagh R, Ghoushchi SJ (2020) HSE risk prioritization of molybdenum operation process using extended FMECA approach based on Fuzzy BWM and Z-WASPAS. J Intell Fuzzy Syst 38(4):5157–5173. https://doi.org/10.3233/JIFS-191749

    Article  Google Scholar 

  2. Akkaya G, Turanoğlu B, Öztaş S (2015) An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing. Expert Syst Appl 42(24):9565–9573

    Article  Google Scholar 

  3. Asady B, Zendehnam A (2007) Ranking fuzzy numbers by distance minimization. Appl Math Model 31(11):2589–2598

    Article  Google Scholar 

  4. Başhan V, Demirel H, Gul M (2020) An FMECA-based TOPSIS approach under single valued neutrosophic sets for maritime risk evaluation: the case of ship navigation safety. Soft Comput 24:18749–18764. https://doi.org/10.1007/s00500-020-05108-y

    Article  Google Scholar 

  5. Baykasoğlu A, Gölcük İ (2017) Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Syst Appl 70:37–51

    Article  Google Scholar 

  6. Boral S, Chaturvedi SK, Howard IM, McKee K, Naikan VNA (2020) An integrated fuzzy failure mode and effect analysis using fuzzy AHP and fuzzy MARCOS. In: 2020 IEEE international conference on industrial engineering and engineering management (IEEM). pp 395–400. https://doi.org/10.1109/IEEM45057.2020.9309790

  7. Boral S, Howard I, Chaturvedi SK, McKee K, Naikan VNA (2020) An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA. Eng Fail Anal 108:104195

    Article  Google Scholar 

  8. Boral S, Howard I, Chaturvedi SK, McKee K, Naikan VNA (2020) A novel hybrid multi-criteria group decision making approach for failure mode and effect analysis: an essential requirement for sustainable manufacturing. Sustain Product Consum 21:14–32

    Article  Google Scholar 

  9. Boral S, Chaturvedi SK, Howard I, Naikan VNA, McKee K (2021) An integrated interval type-2 fuzzy sets and multiplicative half quadratic programming-based MCDM framework for calculating aggregated risk ranking results of failure modes in FMECA. Process Saf Environ Prot 150:194–222

    Article  Google Scholar 

  10. Bowles JB, Peláez CE (1995) Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliab Eng Syst Saf 50:203–213

    Article  Google Scholar 

  11. Bozanic D, Tešić D, Kočić J (2019) Multi-criteria FUCOM–Fuzzy MABAC model for the selection of location for construction of single-span bailey bridge. Decis Mak: Appl Manag Eng 2(1):132–146

    Google Scholar 

  12. Celik E, Bilisik ON, Erdogan M, Gumus AT, Baracli H (2013) An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul. Transp Res Part E 58:28–51

    Article  Google Scholar 

  13. Chatterjee K, Pamucar D, Zavadskas EK (2018) Evaluating the performance of suppliers based on using the R’AMATEL-MAIRCA method for green supply chain implementation in electronics industry. J Clean Prod 184:101–129

    Article  Google Scholar 

  14. Das S, Dhalmahapatra K, Maiti J (2020) Z-number integrated weighted VIKOR technique for hazard prioritization and its application in virtual prototype based EOT crane operations. Appl Soft Comput 94:106419. https://doi.org/10.1016/j.asoc.2020.106419

    Article  Google Scholar 

  15. Fattahi R, Khalilzadeh M (2018) Risk evaluation using a novel hybrid method based on FMECA, extended MULTIMOORA, and AHP methods under fuzzy environment. Saf Sci 102:290–300

    Article  Google Scholar 

  16. Fattahi R, Tavakkoli-Moghaddam R, Khalilzadeh M, Shahsavari-Pour N, Soltani R (2020) A novel FMECA model based on fuzzy multiple-criteria decision-making methods for risk assessment. J Enterp Inf Manag 33(5):881–904. https://doi.org/10.1108/JEIM-09-2019-0282

    Article  Google Scholar 

  17. Fliz M-A, Langner JEB, Herrmann C, Thiede S (2021) Data-driven failure mode and effect analysis (FMECA) to enhance maintenance planning. Comput Ind 129:103451

    Article  Google Scholar 

  18. Ghorabee MK (2016) Develo** an MCDM method for robot selection with interval type-2 fuzzy sets. Robot Comput Integr Manuf 37:221–232

    Article  Google Scholar 

  19. Ghorabaee MK, Zavadskas EK, Amiri M, Esmaeili A (2016) Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. J Clean Prod 137:213–229

    Article  Google Scholar 

  20. Ghoushchi SJ, Gharibi K, Osgooei E, Ab Rahman MN, Khazaeili M (2021) Risk prioritization in failure mode and effects analysis with extended SWARA and MOORA Methods based on Z-numbers theory. Informatica 32(1):41–67. https://doi.org/10.15388/20-INFOR439

    Article  MathSciNet  Google Scholar 

  21. Gul M, Ak MF (2021) A modified failure modes and effects analysis using interval-valued spherical fuzzy extension of TOPSIS method: case study in a marble manufacturing facility. Soft Comput 25(8):6157–6178. https://doi.org/10.1007/s00500-021-05605-8

    Article  Google Scholar 

  22. He S-S, Wang Y-T, Peng J-J, Wang J-Q (2020) Risk ranking of wind turbine systems through an improved FMECA based on probabilistic linguistic information and the TODIM method. J Oper Res Soc 1–14. https://doi.org/10.1080/01605682.2020.1854629

  23. Kutlu AC, Ekmekçioğlu M (2012) Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst Appl 39:61–67

    Article  Google Scholar 

  24. Li J, Fang H, Song W (2019) Modified failure mode and effects analysis under uncertainty: a rough cloud theory-based approach. Appl Soft Comput 78:195–208

    Article  Google Scholar 

  25. Li GF, Li Y, Chen CH, He JL, Hou TW, Chen JH (2020) Advanced FMECA method based on interval 2-tuple linguistic variables and TOPSIS. Qual Eng 32(4):653–662. https://doi.org/10.1080/08982112.2019.1677913

    Article  Google Scholar 

  26. Liu H-C (2016) FMECA using uncertainty theories and MCDM methods. In: FMECA using uncertainty theories and MCDM methods. Springer, Singapore. https://doi.org/10.1007/978-981-10-1466-6_2

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

    Article  Google Scholar 

  28. Liu H-C, Liu L, Liu N (2013) Risk evaluation approaches in failure mode and effect analysis: a literature review. Expert Syst Appl 40(2):828–838

    Article  Google Scholar 

  29. Lo H-W, Shiue W, Liou JJ, Tzeng G-H (2020) A hybrid MCDM-based FMECA model for identification of critical failure modes in manufacturing. Soft Comput 24(20):15733–15745

    Article  Google Scholar 

  30. Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821. https://doi.org/10.1109/TFUZZ.2006.879986

    Article  Google Scholar 

  31. Mzougui I, Carpitella S, Certa A, Felsoufi ZE, Izquierdo J (2020) Assessing supply chain risks in the automotive industry through a modified MCDM-based FMECA. Processes 8(5):579. https://doi.org/10.3390/pr8050579

    Article  Google Scholar 

  32. Opricovic S (2011) Fuzzy VIKOR with an application to water resources planning. Expert Syst Appl 38(10):12983–12990

    Article  Google Scholar 

  33. Pamučar D, Mihajlović M, Obradović R, Atanasković P (2017) Novel approach to group multi-criteria decision making based on interval rough numbers: hybrid DEMATEL-ANP-MAIRCA model. Expert Syst Appl 88:58–80

    Article  Google Scholar 

  34. Pamučar D, Vasin L, Lukovac L (2014) Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARICA model. In: XVI international scientific-expert conference on railway, Railcon, pp 89–92

    Google Scholar 

  35. Pintelon L, Di Nardo M, Murino T, Pileggi G, Vander Poorten E (2021) A new hybrid MCDM approach for RPN evaluation for a medical device prototype. Qual Reliab Eng Int 1–25. https://doi.org/10.1002/qre.2852

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

    Article  Google Scholar 

  37. Sharma RK, Kumar D, Kumar P (2005) Systematic failure mode effect analysis (FMECA) using fuzzy linguistic modelling. Int J Qual Reliab Manag 22:986–1004

    Article  Google Scholar 

  38. Song W, Ming X, Wu Z, Zhu B (2014) A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Qual Reliab Eng Int 30:473–486

    Article  Google Scholar 

  39. Stanković M, Stević Ž, Das DK, Subotić M, Pamučar D (2020) A new fuzzy MARCOS method for road traffic risk analysis. Mathematics 8(3):457. https://doi.org/10.3390/math8030457

    Article  Google Scholar 

  40. Stević Ž, Pamučar D, Puška A, Chatterjee P (2020) Sustainable supplier selection in healthcare industries using a new MCDM method: measurement of alternatives and ranking according to compromise solution (MARCOS). Comput Ind Eng 140:106231

    Article  Google Scholar 

  41. Wang Z, Gao J-M, Wang R-X, Chen K, Gao Z-Y, Zheng W (2018) Failure mode and effects analysis by using the house of reliability-based rough VIKOR approach. IEEE Trans Reliab 67:230–248. https://doi.org/10.1109/TR.2017.2778316

    Article  Google Scholar 

  42. Wu D (2010) A brief tutorial on Interval type-2 fuzzy sets and systems. Fuzzy sets and systems. https://www.researchgate.net/profile/Dongrui-Wu/publication/253502483_A_Brief_Introduction_to_Type2_Fuzzy_Logic/links/55ec4c3e08ae65b6389e5af3/A-Brief-Introduction-to-Type2-Fuzzy-Logic.pdf

  43. Wu D, Mendel JM (2014) Designing practical interval type-2 fuzzy logic systems made simple. In: 2014 international conference on fuzzy systems (FUZZ-IEEE), pp 800–807. https://doi.org/10.1109/FUZZ-IEEE.2014.6891534

  44. Xu Z, Qin J, Liu J, Martinez L (2019) Sustainable supplier selection based on AHPSort II in interval type-2 fuzzy environment. Inf Sci 483:273–293

    Article  Google Scholar 

  45. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

  46. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 8(3):199–249

    Article  MathSciNet  Google Scholar 

  47. Zarbakhshnia N, Soleimani H, Ghaderi H (2018) Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria. Appl Soft Comput 65:307–319

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Civil Engineering, Faculty of Engineering and Physical Science, University of Leeds, Leeds, LS2 9JT, UK

    Soumava Boral & Sanjay K. Chaturvedi

  2. School of Civil and Mechanical Engineering, Curtin University, Perth, 6102, Australia

    Soumava Boral & Ian Howard

  3. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway

    Yiliu Liu

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  1. Soumava Boral
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  2. Sanjay K. Chaturvedi
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  3. Yiliu Liu
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  4. Ian Howard
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Correspondence to Sanjay K. Chaturvedi .

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  1. Swiss Federal Railways (SBB CFF FFS), Bern, Switzerland

    Durga Rao Karanki

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Boral, S., Chaturvedi, S.K., Liu, Y., Howard, I. (2024). Integrated Fuzzy MCDM Frameworks in Risk Prioritization of Failure Modes. In: Karanki, D.R. (eds) Frontiers of Performability Engineering. Risk, Reliability and Safety Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-8258-5_14

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  • DOI: https://doi.org/10.1007/978-981-99-8258-5_14

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