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Literature review and prospect of the development and application of FMEA in manufacturing industry

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Abstract

In order to enable failure mode and effects analysis (FMEA) to play a better quality control role in complex manufacturing products or systems, the current research status of FMEA is reviewed from failure mode identification, risk assessment, and industrial standard application. Firstly, the research status of system failure identification is summarized from the following aspects: the breakthrough point of identification, the types of identification methods, and the normalized description of failure modes. Then, sort out the research status of risk assessment from five aspects: risk factor evaluation criteria, risk assessment opinion expression, expert opinion consensus, risk opinion assessment aggregation, and sensitivity analysis, and find out research hotspots and blind spots; finally, the changes of FMEA standards in various fields are summarized and compared, and the future development trend of FMEA in the context of intelligent manufacturing is discussed.

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References

  1. Lo HW, Liou JJ, Huang CN, Chuang YC (2019) A novel failure mode and effect analysis model for machine tool risk analysis. Reliab Eng Syst Saf 183:173–183

    Article  Google Scholar 

  2. Su X, Deng Y, Mahadevan S (2012) An improved method for risk evaluation in failure modes and effects analysis of aircraft engine rotor blades. Eng Fail Anal 26:164–174

    Article  Google Scholar 

  3. Baghery M, Yousefi S, Rezaee MJ (2018) Risk measurement and prioritization of auto parts manufacturing processes based on process failure analysis, interval data envelopment analysis and grey relational analysis. J Intell Manuf 29(8):1803–1825

    Article  Google Scholar 

  4. Sakthivel G, Ikua BW (2017) Failure mode and effect analysis using fuzzy analytic hierarchy process and GRA TOPSIS in manufacturing industry. Int J Prod Qual Manag 22(4):466–484

    Google Scholar 

  5. Wang Z, Gao J, Wang R (2018) Failure mode and effects analysis using Dempster-Shafer theory and TOPSIS method: application to the gas insulated metal enclosed transmission line (GIL). Appl Soft Comput:633–647

  6. Ahn J, Noh Y, Park SH (2017) Fuzzy-based failure mode and effect analysis (FMEA) of a hybrid molten carbonate fuel cell (MCFC) and gas turbine system for marine propulsion. J Power Sources 364:226–233

    Article  Google Scholar 

  7. Liu HC, You XY, Tsung F (2018) An improved approach for failure mode and effect analysis involving large group of experts: an application to the healthcare field. Qual Eng 30(4):762–775

    Article  Google Scholar 

  8. Wang L, Hu Y P, Liu H C(2019) A linguistic risk prioritization approach for failure mode and effects analysis: a case study of medical product development. Qual Reliab Eng Int DOI:https://doi.org/10.1002/qre.2472, 2019

  9. Mutlu NG, Altuntaş S Hazard and risk analysis for ring spinning yarn production process by integrated FTA-FMEA approach. J Text Apparel/Tekstil ve Konfeksiyon 29(3)

  10. Mutlu NG, Altuntas (2019) Risk analysis for occupational safety and health in the textile industry: integration of FMEA, FTA, and BIFPET methods. Int J Ind Ergon 72:222–240

    Article  Google Scholar 

  11. Altuntas S, Kansu S (2019) An innovative and integrated approach based on SERVQUAL, QFD and FMEA for service quality improvement: a case study. Kybernetes 49:2419–2453. https://doi.org/10.1108/K-04-2019-0269

    Article  Google Scholar 

  12. Liu HC, Liu L, Liu N (2013) Risk evaluation approaches in failure mode and effects analysis: a literature review. Expert Syst Appl 40(2):828–838

    Article  Google Scholar 

  13. Waghmare SN, Raut DN, Mahajan SK, Mahajan SK, Bhamare SS (2014) Failure mode effect analysis and total productive maintenance: a review. Int J Innov Res Adv Eng 1(6):183–184

    Google Scholar 

  14. Spreafico C, Russo D, Rizzi C (2017) A state-of-the-art review of FMEA/FMECA including patents. Comput Sci Rev 25:19–28

    Article  Google Scholar 

  15. Liu H, Chen X, Duan C, Wang YM (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 

  16. Liu HC (2019) FMEA for proactive healthcare risk analysis: a systematic literature review. In: Improved FMEA methods for proactive healthcare risk analysis. Springer, Singapore, pp 15–45

    Chapter  Google Scholar 

  17. Stone RB, Tumer IY, Van Wie M (2005) The function-failure design method. J Mech Des 127(3):397–407

    Article  Google Scholar 

  18. Wang C, Li Y, Li WQ (2013) System failure analysis and creative design based on functional role model. J Mach Des 30(12):1–5

    Google Scholar 

  19. DeStefano CB, Jensen DC (2014) A qualitative failure analysis using function-based performance state-machines for fault identification and propagation during early design phases. In ASME 2014 International design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers Digital Collection

  20. Kurtoglu T, Tumer IY (2008) A graph-based fault identification and propagation framework for functional design of complex systems. J Mech Des 130(5):1–8

    Article  Google Scholar 

  21. O'Halloran BM, Papakonstantinou N, Van Bossuyt DL (2015) Modeling of function failure propagation across uncoupled systems. In 2015 Annual Reliability and Maintainability Symposium (RAMS) IEEE, pp. 1-6

  22. Arunajadai SG, Stone RB, Tumer IY, Clancy D (2002) A Framework for creating a function based design tool for failure mode identification. Paper presented at the ASME 2002 International design engineering technical conferences and computers and information in engineering conference, Montreal, Canada

  23. Zheng HM, Liu WD, Xiao CD (2018) An activity-based defect management framework for product development. Comput Ind Eng 118:202–209

    Article  Google Scholar 

  24. Zheng HM, Liu WD, Xiao CD (2018) Structural relationship model for design defects and influencing factors in the concurrent design process. Int J Prod Res 56(14):4897–4924

    Article  Google Scholar 

  25. Li G, Gao J, Chen F (2009) A novel approach for failure modes and effects analysis based on polychromatic sets. Artif Intell Eng Des Anal Manuf 23(2):119–129

    Article  Google Scholar 

  26. Struss P, Price C (2003) Model based systems in the automotive industry. AI Mag 24(4):1–17

    Google Scholar 

  27. Xu Z, Dang Y, Munro P, Wang Y (2020) A data-driven approach for constructing the component failure mode matrix for FMEA. J Intell Manuf 31(1):249–265

    Article  Google Scholar 

  28. Loganathan MK, Goswami P, Bhagawati B (2016) Failure evaluation and analysis of mechatronics-based production systems during design stage using structural modeling. Appl Mech Mater Trans Tech Publ Ltd 852:799–805

    Article  Google Scholar 

  29. Tumer IY, Stone RB (2003) Mapping function to failure mode during component development. Res Eng Des 14(1):25–33

    Article  Google Scholar 

  30. Loganathan MK, Gandhi MS, Gandhi OP (2015) Functional cause analysis of complex manufacturing systems using structure. Proc Inst Mech Eng B J Eng Manuf 229(3):533–545

    Article  Google Scholar 

  31. Wani MF (2006) Failure analysis of mechanical systems based on function-cum-structure approach. In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers Digital Collection (pp. 975-983)

  32. Feng FZ, Luo JH, Liu YH et al (2016) A FMMEA analysis method based on function-structure-failure model. Journal of Vibration, Measurement & Diagnosis 36(03):413–418 598

    Google Scholar 

  33. Zhang J, Roberts PD (1991) Process fault diagnosis with diagnostic rules based on structural decomposition. J Process Control 1(5):259–269

    Article  Google Scholar 

  34. Nepal BP, Yadav OP, Monplaisir L (2008) A framework for capturing and analyzing the failures due to system/component interactions. Qual Reliab Eng Int 24(3):265–289

    Article  Google Scholar 

  35. Augustine M, Yadav OP, Jain R (2012) Cognitive map-based system modeling for identifying interaction failure modes. Res Eng Des 23(2):105–124

    Article  Google Scholar 

  36. Sun Y, Ma L, Mathew J, Zhang S (2006) An analytical model for interactive failures. Reliab Eng Syst Saf 91(5):495–504

    Article  Google Scholar 

  37. Sun Y, Ma L, Mathew J (2009) Failure analysis of engineering systems with preventive maintenance and failure interactions. Comput Ind Eng 57(2):539–549

    Article  Google Scholar 

  38. Ma H, Chu X, Xue D (2019) Identification of to-be-improved components for redesign of complex products and systems based on fuzzy QFD and FMEA. J Intell Manuf 30(2):623–639

    Article  Google Scholar 

  39. Wang SY (2003) Failure mode and effect analysis (FMEA). Sun yat-sen university press

  40. Bluvband Z, Grabov P (2009) Failure analysis of FMEA. In 2009 Annual Reliability and Maintainability Symposium, IEEE, (pp. 344-347)

  41. Stone R, Tumer IY, Stock ME (2005) Linking product functionality to historic failures to improve failure analysis in design. Res Eng Des 16(1–2):96–108

    Article  Google Scholar 

  42. Zhang X, Li Y, Ran Y, Zhang G (2019) A hybrid multilevel FTA-FMEA method for a flexible manufacturing cell based on meta-action and TOPSIS. IEEE Access 7:110306–110315

    Article  Google Scholar 

  43. Zhang X, Zhang G, Li Y, Wang H, Gong X (2019) A novel fault diagnosis approach of a mechanical system based on meta-action unit. Adv Mech Eng 11(2):1687814019826644

    Article  Google Scholar 

  44. Hurdle EE, Bartlett LM, Andrews JD (2007) System fault diagnostics using fault tree analysis. Proc Inst Mech Eng O J Risk Reliab 221(1):43–55

    Google Scholar 

  45. Deng Y, Li Q, Lu Y (2015) A research on subway physical vulnerability based on network theory and FMECA. Saf Sci 80:127–134

    Article  Google Scholar 

  46. Rao RV, Gandhi OP (2002) Failure cause analysis of machine tools using digraph and matrix methods. Int J Mach Tools Manuf 42(4):521–528

    Article  Google Scholar 

  47. Bartlett LM, Hurdle EE, Kelly EM (2009) Integrated system fault diagnostics utilising digraph and fault tree-based approaches. Reliab Eng Syst Saf 94(6):1107–1115

    Article  Google Scholar 

  48. Chen X, Li WQ, Li Y (2014) Integrated analysis of fishbone and evolution laws on product failure prediction and problem solving. Chin J Eng Des 21(02):109–114

    Google Scholar 

  49. Hata T, Kobayashi N, Kimura F, Suzuki H (2000) Representation of functional relations among parts and its application to product failure reasoning. J Manuf Sci Prod 3(2–4):77–84

    Google Scholar 

  50. Liu L, Fan D, Wang Z, Yang D, Cui J, Ma X, Ren Y (2019) Enhanced GO methodology to support failure mode, effects and criticality analysis. J Intell Manuf 30(3):1451–1468

    Article  Google Scholar 

  51. Arvanitoyannis IS, Varzakas TH (2008) Application of ISO 22000 and failure mode and effect analysis (FMEA) for industrial processing of salmon: a case study. Crit Rev Food Sci Nutr 48(5):411–429

    Article  Google Scholar 

  52. Eubanks F (2012) HAZOP analysis of product requirements for early failure mode identification. In INCOSE International Symposium (Vol. 22, No. 1, pp. 1977-1985)

  53. James AT, Gandhi OP, Deshmukh SG (2018) Fault diagnosis of automobile systems using fault tree based on digraph modeling. Int J Syst Assur Eng Manag 9(2):494–508

    Article  Google Scholar 

  54. Kelly EM, Bartlett LM (2007) Aircraft fuel rig system fault diagnostics based on the application of digraphs. Proc Inst Mech Eng O J Risk Reliab 221(4):275–284

    Google Scholar 

  55. Noh KW, Jun HB, Lee JH (2011) Module based failure propagation (MFP) model for FMEA. Int J Adv Manuf Technol 55(5–8):581–600

    Article  Google Scholar 

  56. Sierla S, Tumer I, Papakonstantinou N (2012) Early integration of safety to the mechatronic system design process by the functional failure identification and propagation framework. Mechatronics 22(2):137–151

    Article  Google Scholar 

  57. Tumer I Y, Stone R B, Bell D G (2003). Requirements for a failure mode taxonomy for use in conceptual design. In DS 31: Proceedings of ICED 03, the 14th International Conference on Engineering Design, Stockholm

  58. Arunajadai SG, Uder SJ, Stone RB (2004) Failure mode identification through clustering analysis. Qual Reliab Eng Int 20(5):511–526

    Article  Google Scholar 

  59. Wu LL, Liu WD, Xiao SH et al (2018) Information extraction method of process components based on fusion of word vector and neural network. J Nanchang Univ (Nat Sci) 42(03):274–282

    Google Scholar 

  60. Zheng HM, Liu WD, Xiao CD, Hu WL (2014) Formation and assessment modeling of mechanical product design defect based on analyzing design activity. Comput Integr Manuf Syst 2015 21(1):31–39

    Google Scholar 

  61. Yang LB, Li P, Xue R, Ma XN, Wu YH, Zou D (2018) Intelligent classification of faults of railway signal equipment based on imbalanced text data mining. J Chin Railw Soc 40(02):59–66

    Google Scholar 

  62. Wani MF, Jan M (2006) Failure mode analysis of mechanical systems at conceptual design stage. In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers Digital Collection, (pp. 985-995)

  63. Chen L, Nayak DR (2007) A case study of failure mode analysis with text mining methods. In Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining-Volume 84 (pp. 49-60). Australian Computer Society, Inc.

  64. Rajpathak DG (2013) An ontology based text mining system for knowledge discovery from the diagnosis data in the automotive domain. Comput Ind 64(5):565–580

    Article  Google Scholar 

  65. Jensen DC, Hoyle C, Tumer IY (2012) Clustering function-based failure analysis results to evaluate and reduce system-level risks. In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference American Society of Mechanical Engineers Digital Collection, (pp. 1055-1064)

  66. Chang WL, Tay KM, Lim CP (2015) Clustering and visualization of failure modes using an evolving tree. Expert Syst Appl 42(20):7235–7244

    Article  Google Scholar 

  67. Jensen DC, Bello O, Hoyle C, Tumer IY (2014) Reasoning about system-level failure behavior from large sets of function-based simulations. AI EDAM 28(4):385–398

    Google Scholar 

  68. Kurtoglu T, Tumer IY, Jensen DC (2010) A functional failure reasoning methodology for evaluation of conceptual system architectures. Res Eng Des 21(4):209–234

    Article  Google Scholar 

  69. Liu SC, Liu SY (2003) An efficient expert system for machine fault diagnosis. Int J Adv Manuf Technol 21(9):691–698

    Article  Google Scholar 

  70. Li B, Han T, Kang F (2013) Fault diagnosis expert system of semiconductor manufacturing equipment using a Bayesian network. Int J Comput Integr Manuf 26(12):1161–1171

    Article  Google Scholar 

  71. Boral S, Chaturvedi SK, Naikan VNA (2019) A case-based reasoning system for fault detection and isolation: a case study on complex gearboxes. J Qual Maint Eng 25(2):213–235

    Article  Google Scholar 

  72. Jacobo VH, Ortiz A, Cerrud Y (2007) Hybrid expert system for the failure analysis of mechanical elements. Eng Fail Anal 14(8):1435–1443

    Article  Google Scholar 

  73. Wang HC, Wang HS (2005) A hybrid expert system for equipment failure analysis. Expert Syst Appl 28(4):615–622

    Article  Google Scholar 

  74. Zhong Z, Xu T, Wang F, Tang T (2018) Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing. Math Probl Eng:2018

  75. Xu F, Liu X, Chen W, Zhou C, Cao B (2018) Ontology-based method for fault diagnosis of loaders. Sensors 18(3):729

    Article  Google Scholar 

  76. Dendani-Hadiby N, Khadir MT (2013) A fault diagnosis application based on a combination case-based reasoning and ontology approach. Int J Knowl Based Intell Eng Syst 17(4):305–317

    Google Scholar 

  77. Thike PH, Xu Z, Cheng Y (2019) Materials failure analysis utilizing rule case based hybrid reasoning method. Eng Fail Anal 95:300–311

    Article  Google Scholar 

  78. Rajpathak D, De S (2016) A data-and ontology-driven text mining based construction of reliability model to analyze and predict component failures. Knowl Inf Syst 46(1):87–113

    Article  Google Scholar 

  79. Wang CY, Jiang QY, Tang YJ (2019) Fault diagnosis of power dispatching based on alarm signal text mining. Electr Power Autom Equip 39(04):126–132

    Google Scholar 

  80. Wang F, Xu T, Tang T, Zhou M, Wang H (2016) Bilevel feature extraction-based text mining for fault diagnosis of railway systems. IEEE Trans Intell Transp Syst 18(1):49–58

    Article  Google Scholar 

  81. Du XM, Qin JF, Guo SY (2018) Text Mining of Typical Defects in power equipment. High Voltage Eng 44(04):1078–1084

    Google Scholar 

  82. Nie WB, Liu WD, et al (2019) Method for automatically identifying process failure mode. China:CN201610373015.1

  83. Abrahams AS, Fan W, Wang GA, Zhang ZJ, Jiao J (2015) An integrated text analytic framework for product defect discovery. Prod Oper Manag 24(6):975–990

    Article  Google Scholar 

  84. Zhang W, Xu H, Wan W (2012) Weakness finder: find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Syst Appl 39(11):10283–10291

    Article  Google Scholar 

  85. Law D, Gruss R, Abrahams AS (2017) Automated defect discovery for dishwasher appliances from online consumer reviews. Expert Syst Appl 67:84–94

    Article  Google Scholar 

  86. Winkler M, Abrahams S, Gruss R, Ehsani JP (2016) Toy safety surveillance from online reviews. Decis Support Syst 90:23–32

    Article  Google Scholar 

  87. Chandrasekaran B, Josephson JR, Benjamins VR (1999) What are ontologies, and why do we need them? IEEE Intell Syst 14(1):20–26

    Article  Google Scholar 

  88. Liu B, Wu J, Yao L, Ding Z (2019) Ontology-based fault diagnosis: a decade in review. In Proceedings of the 11th International Conference on Computer Modeling and Simulation (pp. 112-116)

  89. Mikos WL, Ferreira JC, Botura PE, Freitas LS (2011) A system for distributed sharing and reuse of design and manufacturing knowledge in the PFMEA domain using a description logics-based ontology. J Manuf Syst 30(3):133–143

    Article  Google Scholar 

  90. James AT, Gandhi OP, Deshmukh SG (2017) Knowledge management of automobile system failures through development of failure knowledge ontology from maintenance experience. J Adv Manag Res 14(4):425–445

    Article  Google Scholar 

  91. Ebrahimipour V, Rezaie K, Shokravi S (2010) An ontology approach to support FMEA studies. Expert Syst Appl 37(1):671–677

    Article  Google Scholar 

  92. Xiuxu Z, Yuming Z (2012) Application research of ontology-enabled process FMEA knowledge management method. Int J Intell Syst Appl 4(3):34–40

    Google Scholar 

  93. Rehman Z, Kifor S (2014) A conceptual architecture of ontology based KM system for failure mode and effects analysis. Int J Comput Commun Control 9(4):463–470

    Article  Google Scholar 

  94. Zhou A, Yu D, Zhang W (2015) A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA. Adv Eng Inform 29(1):115–125

    Article  Google Scholar 

  95. Li G (2013) Ontology based reuse of failure modes in existing databases for FMEA: methodology and tool. IEICE Trans Fundam Electron Commun Comput Sci 96(7):1645–1648

    Article  Google Scholar 

  96. Rehman Z, Kifor CV (2016) An ontology to support semantic management of FMEA knowledge. Int J Comput Commun Control 11(4):507–521

    Article  Google Scholar 

  97. Dittmann L, Rademacher T, Zelewski S (2004) Performing FMEA using ontologies. In 18th International Workshop on Qualitative Reasoning. Evanston USA (pp. 209-216)

  98. Lee BH (2001) Using FMEA models and ontologies to build diagnostic models. AI EDAM 15(4):281–293

    MATH  Google Scholar 

  99. Koji Y, Kitamura Y, Mizoguchi R (2005) Ontology based transformation from an extended functional model to FMEA. In ICED 05: 15th International Conference on Engineering Design: Engineering Design and the Global Economy (p. 1984). Engineers Australia

  100. Molhanec M, Zhuravskaya O, Povolotskaya E, Tarba L (2011) The ontology based FMEA of lead free soldering process. In Proceedings of the 2011 34th International Spring Seminar on Electronics Technology (ISSE) (pp. 267-273). IEEE

  101. Zhao X, Zhu Y (2010) Research of FMEA knowledge sharing method based on ontology and the application in manufacturing process. In 2010 2nd International Workshop on Database Technology and Applications (pp. 1-4). IEEE

  102. Gen XL, Chu XN (2009) Risk evaluation method in failure mode and effects analysis based on failure cause-effect chain. Comput Integr Manuf Syst 15(12):2473–2480

    Google Scholar 

  103. Liu WD, Hu K, Zheng HM (2016) PFMEA technology of multi-varieties and small batch customization mode. Comput Integr Manuf Syst 22(06):1485–1493

    Google Scholar 

  104. Braglia M, Fantoni G, Frosolini M (2007) The house of reliability. Int J Qual Reliab Manag 24(4):420–440

    Article  Google Scholar 

  105. Guide to Failure Mode Impact and Hazard Analysis (2006) GJB/Z 1391—2006. General armament department of the PLA, BeiJing

    Google Scholar 

  106. Nie WB, Liu WD, Xiao SH et al (2018) The construction and optimization of dimension system of severity evaluation of manufacturing process failure modes. Sci Technol Manag Res 38(03):205–212

    Google Scholar 

  107. Certa A, Hopps F, Inghilleri R, La Fata CM (2017) 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

    Article  Google Scholar 

  108. Liu HC, You JX, Lin QL, 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):701–714

    Article  Google Scholar 

  109. Nazeri A, Naderikia R (2017) A new fuzzy approach to identify the critical risk factors in maintenance management. Int J Adv Manuf Technol 92(9–12):3749–3783

    Article  Google Scholar 

  110. Baykasoğlu A, Gölcük İ (2017) Comprehensive fuzzy FMEA model: a case study of ERP implementation risks. Oper Res:1–32

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

    Article  Google Scholar 

  112. Liu HC, Chen YZ, You JX, Li H (2016) Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach. J Intell Manuf 27(4):805–816

    Article  Google Scholar 

  113. Chen JK (2017) Prioritization of corrective actions from utility viewpoint in FMEA application. Qual Reliab Eng Int 33(4):883–894

    Article  Google Scholar 

  114. Kutlu AC, MEkmekçioğlu M (2012) Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst Appl 39(1):61–67

    Article  Google Scholar 

  115. Mandal S, Maiti J (2014) Risk analysis using FMEA: fuzzy similarity value and possibility theory based approach. Expert Syst Appl 41(7):3527–3537

    Article  Google Scholar 

  116. Ghoushchi SJ, Yousefi S, Khazaeili M (2019) An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Appl Soft Comput 81:1–13

    Article  Google Scholar 

  117. Liu HC, Liu L, Bian QH, Lin QL, Dong N, Xu PC (2011) Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Syst Appl 38(4):4403–4415

    Article  Google Scholar 

  118. Deng X, Jiang W (2017) Fuzzy risk evaluation in failure mode and effects analysis using a D numbers based multi-sensor information fusion method. Sensors 17(9):2086

    Article  Google Scholar 

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

    Article  Google Scholar 

  120. Liu S, Guo X, Zhang L (2019) An improved assessment method for FMEA for a shipboard integrated electric propulsion system using fuzzy logic and DEMATEL theory. Energies 12(16):3162

    Article  Google Scholar 

  121. Carpitella S, Certa A, Izquierdo J, La Fata CM (2018) A combined multi-criteria approach to support FMECA analyzes: a real-world case. Reliab Eng Syst Saf 169:394–402

    Article  Google Scholar 

  122. Helvacioglu S, Ozen E (2014) Fuzzy based failure modes and effect analysis for yacht system design. Ocean Eng 79:131–141

    Article  Google Scholar 

  123. Zhang Z, Chu X (2011) Risk prioritization in failure mode and effects analysis under uncertainty. Expert Syst Appl 38(1):206–214

    Article  Google Scholar 

  124. Liu HC, Liu L, Liu N, Mao LX (2012) Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Syst Appl 39(17):12926–12934

    Article  Google Scholar 

  125. Gargama H, Chaturvedi SK (2011) Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Trans Reliab 60(1):102–110

    Article  Google Scholar 

  126. Gupta G, Mishra RP (2017) A failure mode effect and criticality analysis of conventional milling machine using fuzzy logic: case study of RCM. Qual Reliab Eng Int 33(2):347–356

    Article  Google Scholar 

  127. Liu HC, You JX, Duan CY (2019) An integrated approach for failure mode and effect analysis under interval-valued intuitionistic fuzzy environment. Int J Prod Econ 207:163–172

    Article  Google Scholar 

  128. Liu HC, You JX, Shan MM, Shao LN (2015) Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach. Soft Comput 19(4):1085–1098

    Article  Google Scholar 

  129. Foroozesh N, Tavakkoli-Moghaddam R, Mousavi SM (2018) Sustainable-supplier selection for manufacturing services: a failure mode and effects analysis model based on interval-valued fuzzy group decision-making. Int J Adv Manuf Technol 95(9–12):3609–3629

    Article  Google Scholar 

  130. Zhao H, You JX, Liu HC (2017) Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment. Soft Comput 21(18):5355–5367

    Article  Google Scholar 

  131. Guo J (2016) A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory. J Intell Fuzzy Syst 30(2):869–881

    Article  MATH  Google Scholar 

  132. Chang KH, Cheng CH (2010) A risk assessment methodology using intuitionistic fuzzy set in FMEA. Int J Syst Sci 41(12):1457–1471

    Article  MathSciNet  Google Scholar 

  133. Liu HC, Liu L, Li P (2014) Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator. Int J Syst Sci 45(10):2012–2030

    Article  MathSciNet  MATH  Google Scholar 

  134. Chang KH, Wen TC, Chung HY (2018) Soft failure mode and effects analysis using the OWG operator and hesitant fuzzy linguistic term sets. J Intell Fuzzy Syst 34(4):2625–2639

    Article  Google Scholar 

  135. Duan CY, Chen XQ, Shi H, Liu HC (2019) A new model for failure mode and effects analysis based on k-means clustering within hesitant linguistic environment. IEEE Trans Eng Manag 2019:1–11. https://doi.org/10.1109/TEM.2019.2937579

    Article  Google Scholar 

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

    Article  Google Scholar 

  137. Li XY, Wang ZL, Xiong Y, Liu HC (2019) A novel failure mode and effect analysis approach integrating probabilistic linguistic term sets and fuzzy petri nets. IEEE Access 7:54918–54928

    Article  Google Scholar 

  138. Huang J, Liu HC, Duan CY, Song MS (2019) An improved reliability model for FMEA using probabilistic linguistic term sets and TODIM method. Ann Oper Res:1–24

  139. Liu HC, You JX, You XY (2014) Evaluating the risk of healthcare failure modes using interval 2-tuple hybrid weighted distance measure. Comput Ind Eng 78:249–258

    Article  Google Scholar 

  140. Chang KH (2016) Generalized multi-attribute failure mode analysis. Neurocomputing 175:90–100

    Article  Google Scholar 

  141. Chang KH, Wen TC (2010) A novel efficient approach for DFMEA combining 2-tuple and the OWA operator. Expert Syst Appl 37(3):2362–2370

    Article  Google Scholar 

  142. Bozdag E, Asan U, Soyer A, Serdarasan S (2015) Risk prioritization in failure mode and effects analysis using interval type-2 fuzzy sets. Expert Syst Appl 42(8):4000–4015

    Article  Google Scholar 

  143. Liu HC, Li P, You JX, Chen YZ (2015) A novel approach for FMEA: combination of interval 2-tuple linguistic variables and gray relational analysis. Qual Reliab Eng Int 31(5):761–772

    Article  Google Scholar 

  144. Li GF, Li Y, Chen CH, He JL, Hou TW, Chen JH (2019) Advanced FMEA method based on interval 2-tuple linguistic variables and TOPSIS. Qual Eng:1–10

  145. Wang L, Hu YP, Liu HC, Shi H (2019) A linguistic risk prioritization approach for failure mode and effects analysis: a case study of medical product development. Qual Reliab Eng Int 35(6):1735–1752

    Article  Google Scholar 

  146. Liu HC, Hu YP, Wang JJ, Sun M (2018) Failure mode and effects analysis using two-dimensional uncertain linguistic variables and alternative queuing method. IEEE Trans Reliab 68(2):554–565

    Article  Google Scholar 

  147. Hu YP, You XY, Wang L, Liu HC (2019) An integrated approach for failure mode and effect analysis based on uncertain linguistic GRA–TOPSIS method. Soft Comput 23(18):8801–8814

    Article  Google Scholar 

  148. Liu HC, Wang LE, You XY (2019) Failure mode and effect analysis with extended grey relational analysis method in cloud setting. Total Qual Manag Bus Excell 30(7–8):745–767

    Article  Google Scholar 

  149. Liu HC, Wang LE, Li Z, Hu YP (2018) Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. IEEE Trans Fuzzy Syst 27(1):84–95

    Article  Google Scholar 

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

    Article  Google Scholar 

  151. 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):357–368

    Article  Google Scholar 

  152. Li Z, Chen L (2019) A novel evidential FMEA method by integrating fuzzy belief structure and grey relational projection method. Eng Appl Artif Intell 77:136–147

    Article  Google Scholar 

  153. Chen L, Deng Y (2018) A new failure mode and effects analysis model using Dempster–Shafer evidence theory and grey relational projection method. Eng Appl Artif Intell 76:13–20

    Article  Google Scholar 

  154. Li Z, Xiao F, Fei L, Mahadevan S, Deng Y (2017) An evidential failure mode and effects analysis using linguistic terms. Qual Reliab Eng Int 33(5):993–1010

    Article  Google Scholar 

  155. Huang J, Li ZS, Liu HC (2017) New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method. Reliab Eng Syst Saf 167:302–309

    Article  Google Scholar 

  156. Lo HW, Liou JJH (2018) A novel multiple-criteria decision-making-based FMEA model for risk assessment. Appl Soft Comput 73:684–696

    Article  Google Scholar 

  157. Shi H, Wang L, Li XY, Liu HC (2019) A novel method for failure mode and effects analysis using fuzzy evidential reasoning and fuzzy Petri nets. J Ambient Intell Humaniz Comput:1–15

  158. Can GF (2018) An intutionistic approach based on failure mode and effect analysis for prioritizing corrective and preventive strategies. Hum Factors Ergon Manuf Serv Ind 28(3):130–147

    Article  Google Scholar 

  159. Mahmoudi M, Amoozad Mahdiraji H, Jafarnejad A, Safari H (2019) Dynamic prioritization of equipment and critical failure modes: an interval-valued intuitionistic fuzzy condition-based model. Kybernetes 48:1913–1941

    Article  Google Scholar 

  160. Nie WB, Liu WD, Wu ZY, Chen BS, Wu LL (2019) Failure mode and effects analysis by integrating Bayesian fuzzy assessment number and extended gray relational analysis-technique for order preference by similarity to ideal solution method. Qual Reliab Eng Int 35(6):1676–1697

    Article  Google Scholar 

  161. Liu A, Qiu H, Lu H, Guo X (2019) A consensus model of probabilistic linguistic preference relations in group decision making based on feedback mechanism. IEEE Access 7:148231–148244

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

  163. Zhang H, Xiao J, Dong Y (2019) Integrating a consensus-reaching mechanism with bounded confidences into failure mode and effect analysis under incomplete context. Knowl-Based Syst 183:104873

    Article  Google Scholar 

  164. Zhang H, Dong Y, Palomares-Carrascosa I, Zhou H (2018) Failure mode and effect analysis in a linguistic context: a consensus-based multi-attribute group decision-making approach. IEEE Trans Reliab 68(2):566–582

    Article  Google Scholar 

  165. Wang R, Li Y, Zhu J et al (2018) Improved FMEA method for risk evaluation considering expert consensus. J Zhejiang Univ (Eng Sci) 52(6):1058–1067

    Google Scholar 

  166. Zhu J, Wang R, Li Y (2018) Failure mode and effects analysis considering consensus and preferences interdependence. Algorithms 11(4):34

    Article  MathSciNet  MATH  Google Scholar 

  167. Dong Y, Zhang H, Herrera-Viedma E (2016) Integrating experts' weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors. Decis Support Syst 84:1–15

    Article  Google Scholar 

  168. Baynal KASIM, Sarı T, Akpınar B (2018) Risk management in automotive manufacturing process based on FMEA and grey relational analysis: a case study. Adv Prod Eng Manag 13(1):69–80

    Google Scholar 

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

    Article  Google Scholar 

  170. Chang KH, Chang YC, Lee YT (2014) Integrating TOPSIS and DEMATEL methods to rank the risk of failure of FMEA. Int J Inf Technol Decis Mak 13(06):1229–1257

    Article  Google Scholar 

  171. Kutlu AC (2012) Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Pergamon Press, Inc. 39 61–67

  172. Chang KH, Cheng CH (2011) Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. J Intell Manuf 22(2):113–129

    Article  Google Scholar 

  173. Jiang W, Xie C, Wei B, Tang Y (2017) Failure mode and effects analysis based on Z-numbers. Intell Autom Soft Comput:1–8

  174. Yang J, Huang HZ, He LP, Zhu SP, Wen D (2011) Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty. Eng Fail Anal 18(8):2084–2092

    Article  Google Scholar 

  175. Du Y, Lu X, Su X, Hu Y, Deng Y (2016) New failure mode and effects analysis: an evidential downscaling method. Qual Reliab Eng Int 32(2):737–746

    Article  Google Scholar 

  176. Emovon I, Norman RA, Alan JM (2015) An integrated multicriteria decision making methodology using compromise solution methods for prioritising risk of marine machinery systems. Ocean Eng 105:92–103

    Article  Google Scholar 

  177. Mohsen O, Fereshteh N (2017) An extended VIKOR method based on entropy measure for the failure modes risk assessment – a case study of the geothermal power plant (GPP). Saf Sci 92:160–172

  178. Akbarzade Khorshidi H, Gunawan I, Ibrahim MY (2016) Applying UGF concept to enhance the assessment capability of FMEA. Qual Reliab Eng Int 32(3):1085–1093

    Article  Google Scholar 

  179. Al Mashaqbeh S, Munive-Hernandez JE, Khurshid Khan M (2019) Using EWGM method to optimise the FMEA as a risk assessment methodology. Concurr Eng 27(2):144–154

    Article  Google Scholar 

  180. Gu Y, Fu Y, Liang L (2016) Sensitivity analysis method of failure modes and effect analysis based on TOPSIS theory. J China Coal Soc 41(S2):598–604

    Google Scholar 

  181. Chen H (2016) Sensitivity analysis of random evaluation parameters for process failure risk assessment. (doctoral dissertation)

  182. Chen B S, (2019) Risk assessment model and sensitivity analysis of failure mode of small batch custom production process. (doctoral dissertation)

  183. US Department of Defense (1980) Procedures for performing a failure mode, effects and criticality analysis. MIL-STD-1629. Washington (DC)

  184. Mikulak RJ, McDermott R, Beau M The basics of FMEA 2nd Edition

  185. AIAG (2002) FMEA-3 potential failure mode and effects analysis, 2nd edn. Automotive Industry Action Group (AIAG)

  186. JEP131A (2005) Potential Failure Mode and Effects Analysis (FMEA).JEDEC Solid State Technology Association

  187. Automotive industry action group (AIAG) Potential failure mode and effect analysis (FMEA) Reference Manual, FMEA reference manual 4th edition, 2008

  188. SAE J1739 (2011) Potential failure mode and effects analysis in design (Design FMEA) and potential failure mode and effects analysis in manufacturing and assembly processes (Process FMEA) and Effects Analysis for Machinery (Machinery FMEA). SAE Standard

  189. Automotive Industry Action Group, Verband Der Automobilindustrie (2019) AIAG-VDA failure mode and effects analysis (FMEA) handbook. Automotive Industry Action Group, Southfield

    Google Scholar 

  190. Liu WD, Chen BS, Nie WB, Wu LL (2018) Process failure mode and effects analysis based on generalized trapezoidal fuzzy. Ind Eng Manag 23(03):33–41

    Google Scholar 

  191. Anes V, Henriques E, Freitas M, Reis L (2018) A new risk prioritization model for failure mode and effects analysis. Qual Reliab Eng Int 34(4):516–528

    Article  Google Scholar 

  192. Garcia PAA, Junior L, Curty I (2013) A weight restricted DEA model for FMEA risk prioritization. Production 23(3):500–507

    Article  Google Scholar 

  193. Ouyang L, Zheng W, Zhu Y (2019) An interval probability-based FMEA model for risk assessment: a real-world case. Qual Reliab Eng Int 36(1):125–143

    Article  Google Scholar 

  194. Zhou Y, Xia J, Zhong Y, Pang J (2016) An improved FMEA method based on the linguistic weighted geometric operator and fuzzy priority. Qual Eng 28(4):491–498

    Article  Google Scholar 

  195. Park J, Park C, Ahn S (2018) Assessment of structural risks using the fuzzy weighted Euclidean FMEA and block diagram analysis. Int J Adv Manuf Technol 99(9–12):2071–2080

    Article  Google Scholar 

  196. Geramian A, Shahin A, Minaei B, Antony J (2019) Enhanced FMEA: an integrative approach of fuzzy logic-based FMEA and collective process capability analysis. J Oper Res Soc:1–13

  197. Nie RX, Tian ZP, Wang XK, Wang JQ, Wang TL (2018) Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment. Knowl-Based Syst 162:185–201

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Acknowledgments

We thank the anonymous reviews and editors for the constructive comments.

Funding

This research is supported by the National Natural Science Foundation of China [grant number 71661023].

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  1. School of Mechanical and Electrical Engineering, Nanchang University, Nanchang, 330031, China

    Zhongyi Wu & Weidong Liu

  2. College of Economics and Management, Nanchang Hangkong University, Nanchang, 330024, China

    Wenbin Nie

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Wu, Z., Liu, W. & Nie, W. Literature review and prospect of the development and application of FMEA in manufacturing industry. Int J Adv Manuf Technol 112, 1409–1436 (2021). https://doi.org/10.1007/s00170-020-06425-0

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