Automotive industries require effective and reliable maintenance strategies to ensure high levels of availability and safety. Risk-based maintenance approach is a useful tool for maintenance decision making with the aim of reducing the overall risk in operating activities. In this paper, a Failure Mode and Effect Analysis (FMEA) model as one of the risk assessment techniques is developed with subjective information derived from domain experts. To overcome the drawbacks of traditional FMEA for risk priority number (RPN) estimation, a linguistic fuzzy set theory, through effective decision attributes in complex automotive equipment is conducted. The main attributes of this approach include the effect of experts’ traits, scales variation, using various membership functions and defuzzification algorithms on reliable Fuzzy-RPN (FRPN) estimation. The result of the proposed model revealed that altering membership functions and defuzzification algorithms had no significant effect on the FRPN estimation, but their values are highly affected by the number of scales. The sensitivity analysis showed that experts’ traits have no sensible impact on experts’ opinion for FRPN estimation, while the detectability index has more impact on FRPN variation. The result of risk classification number showed that the maintenance decision making could be included for the failure modes with the highest RPN values as a priority, which it would be useful to achieve the high level of availability and safety.
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Bahill AT, Smith ED (2009) An industry standard risk analysis technique. Eng Manag J 21(4):16–29
Banduka N, Veža I, Bilić B (2016) An integrated lean approach to process failure mode and effect analysis (PFMEA): a case study from automotive industry. Adv Prod Eng Manag 11(4):355–365. https://doi.org/10.14743/apem2016.4.233
Bertolini M, Bevilacqua M, Massini R (2006) FMECA approach to product traceability in the food industry. Food Control 17(2):137–145
Bona GD, Forcina A, Falcone D (2018) Maintenance strategy design in a sintering plant based on a multicriteria approach. Int J Manag 17(1):29–49
Cagliano AC, Grimaldi S, Rafele C (2011) A systemic methodology for risk management in healthcare sector. Saf Sci 49(5):695–708
Carbone TA, Tippett DD (2004) Project risk management using the project risk FMEA. Eng Manag J 16(4):28–35
Chanamool N, Naenna T (2016) Fuzzy FMEA application to improve decision-making process in an emergency department. Appl Soft Comput 43:441–453
Chen S-J, Hwang C-L (1992) Fuzzy multiple attribute decision making methods. In: Fuzzy multiple attribute decision making. Springer, pp 289–486
Dağsuyu C, Göçmen E, Narlı M, Kokangül A (2016) Classical and fuzzy FMEA risk analysis in a sterilization unit. Comput Ind Eng 101:286–294
Di Bona G, Silvestri A, Forcina A, Petrillo A (2018) Total efficient risk priority number (TERPN): a new method for risk assessment. J Risk Res 21(11):1384–1408
Feili HR, Akar N, Lotfizadeh H, Bairampour M, Nasiri S (2013) Risk analysis of geothermal power plants using failure modes and effects analysis (FMEA) technique. Energy Convers Manag 72:69–76
Guimarães ACF, Lapa CMF (2007) Fuzzy inference to risk assessment on nuclear engineering systems. Appl Soft Comput 7(1):17–28
Gupta S, Bhattacharya J (2007) Reliability analysis of a conveyor system using hybrid data. Qual Reliab Eng Int 23(7):867–882
Hekmatpanah M, Shahin A, Ravichandran N (2011) The application of FMEA in the oil industry in Iran: the case of four litre oil canning process of Sepahan Oil Company. Afr J Bus Manag 5(7):3019
Helvacioglu S, Ozen E (2014) Fuzzy based failure modes and effect analysis for yacht system design. Ocean Eng 79:131–141
Hsu H-M, Chen C-T (1996) Aggregation of fuzzy opinions under group decision making. Fuzzy Sets Syst 79(3):279–285
Johnson K, Khan MK (2003) A study into the use of the process failure mode and effects analysis (PFMEA) in the automotive industry in the UK. J Mater Process Technol 139(1):348–356
Kang HG, Kim MC, Lee SJ, Lee HJ, Eom HS, Choi JG, Jang S-C (2009) An overview of risk quantification issues for digitalized nuclear power plants using a static fault tree. Nucl Eng Technol 41(6):849–858
Keskin GA, Özkan C (2009) An alternative evaluation of FMEA: fuzzy ART algorithm. Qual Reliab Eng Int 25(6):647–661
Kirkire MS, Rane SB, Jadhav JR (2015) Risk management in medical product development process using traditional FMEA and fuzzy linguistic approach: a case study. J Ind Eng Int 11(4):595–611
Kolich M (2014) Using failure mode and effects analysis to design a comfortable automotive driver seat. Appl Ergon 45(4):1087–1096
Liu H-C, You J-X, Lin Q-L, Li H (2015a) Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory. Int J Comput Integr Manuf 28(7):701–714
Liu HC, Li P, You JX, Chen YZ (2015b) A novel approach for FMEA: combination of interval 2-tuple linguistic variables and gray relational analysis. Qual Reliab Eng Int 31(5):761–772
Liu H-C, Chen Y-Z, You J-X, Li H (2016) Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach. J Intell Manuf 27(4):805–816
Nicolis JS, Tsuda I (1985) Chaotic dynamics of information processing: the “magic number seven plus-minus two” revisited. Bull Math Biol 47(3):343–365
Pollard SJ, Strutt J, MacGillivray BH, Hamilton PD, Hrudey SE (2004) Risk analysis and management in the water utility sector: a review of drivers, tools and techniques. Process Saf Environ Prot 82(6):453–462
Press D (2003) Guidelines for failure mode and effects analysis (FMEA), for automotive, aerospace, and general manufacturing industries. CRC Press, Boca Raton
Preyssl C (1995) Safety risk assessment and management—the ESA approach. Reliab Eng Syst Saf 49(3):303–309
Semp BW, Pathan A, Dessert PE (2006) The role of automated FMEA in automotive reliability improvement. In: SAE technical paper
Sharma RK, Sharma P (2015) Qualitative and quantitative approaches to analyse reliability of a mechatronic system: a case. J Ind Eng Int 11(2):253–268
Soltanali H, Garmabaki A, Thaduri A, Parida A, Kumar U, Rohani A (2018) Sustainable production process: an application of reliability, availability, and maintainability methodologies in automotive manufacturing. Proc Inst Mech Eng Part O J Risk Reliab 233:682–697. https://doi.org/10.1177/1748006x18818266
Verma AK, Ajit S, Karanki DR (2010) Reliability and safety engineering, vol 43. Springer, Berlin
Vinodh S, Santhosh D (2012) Application of FMEA to an automotive leaf spring manufacturing organization. TQM J 24(3):260–274
Woodhouse S (2005) Engineering for safety: use of failure mode and effects analysis in the laboratory: a well-known engineering tool now being used to assure patient safety. Lab Med 36(1):16–18
Yazdi M, Soltanali H (2018) Knowledge acquisition development in failure diagnosis analysis as an interactive approach. Int J Interact Des Manuf 13:193–210
Yazdi M, Daneshvar S, Setareh H (2017) An extension to fuzzy developed failure mode and effects analysis (FDFMEA) application for aircraft landing system. Saf Sci 98:113–123
Zhou Q, Thai VV (2016) Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction. Saf Sci 83:74–79
The financial support provided by the Ferdowsi University of Mashhad (Project No. 43956) is duly acknowledged.
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Soltanali, H., Rohani, A., Abbaspour-Fard, M.H. et al. Development of a risk-based maintenance decision making approach for automotive production line. Int J Syst Assur Eng Manag 11, 236–251 (2020). https://doi.org/10.1007/s13198-019-00927-1
- Automotive industry
- Fuzzy set theory
- Maintenance decision making
- RPN value
- Sensitivity analysis