Abstract
Fault tree analysis (FTA) is a widely used method for analyzing a system’s failure logic and calculating overall reliability. However, application of conventional FTA has some shortcomings, e.g. in handling the uncertainties, allowing the use of linguistic variables, and integrating human error in failure logic model. Hence, Fuzzy set theory has been proposed to overcome the limitation of conventional FTA. Fuzzy logic provides a framework whereby basic notions such as similarity, uncertainty and preference can be modeled effectively. The aim of this paper is to present a review of the concept of fuzzy theory with fault tree analysis and their applications since 1981, to reflect the current status of Fuzzy fault tree analysis (FFTA) methodologies, their strengths, weaknesses, and their applications. This paper explains the fundamentals of fuzzy theory and describes application of fuzzy importance for using FFTA. The concept of the failure possibility and uncertainty analysis by using FFTA is discussed, and concludes with discussion on the application of FFTA in different fields. The review reveals the effectiveness of the FFTA in comparison with conventional FTA, when there is inadequate amount of accurate reliability oriented information.
Similar content being viewed by others
References
Abdelgawad M (2011) Fuzzy reliability analyzer: quantitative assessment of risk events in the construction industry using fuzzy fault-tree analysis. J Constr Eng Manag 137(4):294–302
Balog E, Berta I (2001) Fuzzy solutions in electrostatics. J. Electrost 51–52(1–4):409–415
Batzias F (2010) Solving river pollution problems by means of fuzzy fault tree analysis., pp 228–233
Batzias A and Batzias F (2003) Fuzzy fault tree analysis as a mechanism for technical support to small/medium electroplaters on a quasi online/real-time basis. In: IEEE international conference on industrial technology, vol 1, p 36–41
Batzias F and Siontorou CG (2004) Investigating the causes of biosensor SNR decrease by means of fault tree analysis. In: Presented at instrumentation and measurement technology conference, IMTC 04. Proceedings of the 21st IEEE, 2004
Bian X, Mou C, Yan Z and Xu J (2009) Reliability analysis of AUV based on fuzzy fault tree. In: Presented at international conference on mechatronics and automation, 2009
Bowles JB, Pelaez CE (1995) Application of fuzzy logic to reliability engineering. Proc IEEE 83(3):435–449
Cai KY (1996a) Introduction to fuzzy reliability. Kluwer Academic, Boston
Cai KY (1996b) System failure engineering and fuzzy methodology an introductory overview. Fuzzy Sets Syst 83(2):113
Celik M (2010) A risk-based modelling approach to enhance shipping accident investigation. Saf Sci 48(1):18–27
Chanda R, Bhattacharjee PK (1998) A reliability approach to transmission expansion planning using fuzzy fault-tree model. Electr Power Syst Res 45(2):101–108
Chang J (2006) The reliability of general vague fault-tree analysis on weapon systems fault diagnosis. Soft Comput 10(7):531–542
Chang K (2009) A novel general approach to evaluating the PCBA for components with different membership function. Appl Soft Comput 9(3):1044–1056
Chang S, Chang C (2003) A fuzzy-logic based fault diagnosis strategy for process control loops. Chem Eng Sci 58(15):3395–3411
Chen L, Shinan C (2011) An approach of fault diagnosis for electronic system of aircraft based on trapezoid fuzzy fault tree. Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on. Deng Leng, China, IEEE
Cheng S, Lin B, Hsu B, Shu M (2009) Fault-tree analysis for liquefied natural gas terminal emergency shutdown system. Expert Syst Appl 36(9):11918–11924
Cremona YGC (1997) The possibilistic reliability theory: theoretical aspects and applications. Struct Saf 19:173–201
Weber DP (1994) Fuzzy fault tree analysis. In: Fuzzy systems 1994b, IEEE World congress. Proceedings of the third IEEE conference on computational intelligence, vol 3, p 1899–1904
Deshpande A (2011) Fuzzy fault tree analysis: revisited. Int J Syst Assur Eng Manag 2(1):3–13
Deshpande AW, Khanna P (1995) Fuzzy fault tree analysis: case studies reliability and safety analysis under fuzziness. Physica, Heidelberg
Ding Y, Yu D (2005) Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis. J Loss Prev Process Ind 18:83–88
Dokas I, Karras D, Panagiotakopoulos D (2009) Fault tree analysis and fuzzy expert systems: early warning and emergency response of landfill operations. Environ Model Softw 24:8–25
Dong-an Z, Jun-jie Z and Ye-wei Z (2010) Risk analysis of shield tunnel segment failure based on fuzzy fault tree method. In: Presented at (ICNC), 2010 sixth international conference on natural computation 2010
Dunyak JP and Wunsch D (1998) Fuzzy probability for system reliability. In: Presented at proceedings of the 37th IEEE conference on decision and control 1998
El-Iraki A and Odoom ER (1998) Fuzzy probist reliability assessment of repairable systems. In: Presented at fuzzy information processing society—NAFIPS, 1998 conference of the North American
Ferdous R (2011a) Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations. Risk Anal 31(1):86–107
Ferdous R, Khan F, Sadiq R, Amyotte P, Veitch B (2011) Analyzing system safety and risks under uncertainty using a bow-tie diagram: an innovative approach. Process Saf Environ Prot 91(1–2):1–18
Ferdous R, Khana F, Veitcha B, Amyotte PR (2009) Methodology for computer aided fuzzy fault tree analysis. Process Saf Environ Prot 87(4):217–226
Fujino T and Hadipriono FC (1994) New gate operations of fuzzy fault tree analysis. In: Proceedings of the third IEEE conference on fuzzy systems, vol 2, p 1246–1251
Furuta H, Shiraishi N (1984) Fuzzy importance in fault tree analysis. Fuzzy Sets Syst 12(3):205–213
Mao G, TU J and Du H (2010) Reliability evaluation based on fuzzy fault tree. In: 2010 IEEE 17th international conference on industrial engineering and engineering management (IE&EM), p 963–966
GazdikI I (1985) Fault diagnosis and prevention by fuzzy sets. IEEE Trans Reliab 34(4):382
Geymayr JAB (1995) Fault-tree analysis: a knowledge-engineering approach. IEEE Trans Reliab 44(1):37–45
Gmytrasiewicz P, Hassberger JA, Lee JC (1990) Fault tree based diagnostics using fuzzy logic. IEEE Trans Pattern Anal Mach Intell 12:1115–1119
Guimarães ACF, Lapa C (2008) Parametric fuzzy study for effects analysis of age on PWR containment cooling system. Appl Soft Comput 8(4):1562–1571
Guimarees A, Ebecken N (1999) Fuzzy FTA: a fuzzy fault tree system for uncertainty analysis. Ann Nucl Energy 26(6):523–532
Guohuan L, Yuan Z and Zheng Y (2010) Study of hybrid intelligent fault diagnosis. In: Presented at 2nd International Asia conference on, informatics in control, automation and robotics (CAR), 2010
Gupta S, Bhattacharya J (2007) Reliability analysis of a conveyor system using hybrid data. Qual Reliab Eng Int 23(7):867
Hatoyama Y (1979) Reliability analysis of 3-state systems. Reliab IEEE Trans 28(R-5):386–393
He L, Huang H and Zuo MJ (2007) Fault tree analysis based on fuzzy logic. In: Reliability and maintainability symposium, RAMS ‘07. p 7–82
Hong YY, Lee LH, Cheng HH (2008) Application of fuzzy fault-tree analysis to assess the reliability of a protection system for a switchyard. Int J Emerg Electr Power Syst 9(4). doi:10.2202/1553-779X.1944
Hu J (2011) Risk identification of sudden water pollution on fuzzy fault tree in beibu-gulf economic zone. Proc Environ Sci 10:2413–2419
Huang HZ, Xin Tonga, Zuo Ming J (2004) Posbist fault tree analysis of coherent systems. Reliab Eng Syst Saf 84(2):141
Hurdle EE, Bartlett LM, Andrews JD (2007) System fault diagnostics using fault tree analysis. Proceedings of the Institution of Mechanical Engineers, Part O: J Risk Reliab 221(1):43–55
Isermann R (1997) Supervision, fault-detection and fault-diagnosis methods—an introduction. Control Eng Pract 5(5):639–652
Isermann R (2006) Fault-diagnosis systems. Springer, Berlin
Jinhua M, Yanfeng L, Haiqing L, Weiwen P and Hong-Zhong H (2011) Reliability analysis of CNC hydraulic system based on fuzzy fault tree. In: Presented at 2011 international conference on quality, reliability, risk, maintenance, and safety engineering (ICQR2MSE) 2011
Kenarangui R (1991) Event-tree analysis by fuzzy probability. Reliab IEEE Trans 40:120–124
Khan FI (2000) Analytical simulation and PROFAT II: a new methodology and a computer automated tool for fault tree analysis in chemical process industries. J Hazard Mater 75(1):1
Kim C (1996) Multilevel fault tree analysis using fuzzy numbers. Comput Oper Res 23(7):695–703
Kumar N (2012a) Reliability analysis of waste clean-up manipulator using genetic algorithms and fuzzy methodology. Comput Oper Res 39(2):310–319
Kumar M (2012b) Reliability analysis of computer security system based on intuitionistic fuzzy fault tree. Adv Mater Res 403–408:3495–3502
Kumar EV, Chaturvedi SK, Deshpande AW (2008) Failure Probability Estimation using Fuzzy Fault Tree Analysis (FFTA) with PDM Data in Process Plants. Int J Perform Eng 4:271
La S, Jianping L and Min Q (2008) Study on applying fault tree analysis based on fuzzy reasoning in risk analysis of construction quality. In: International conference on risk management & engineering management, ICRMEM ‘08, p 393–397
Lee WS, Grosh DL, Tillman FA, Lie CH (1985) Fault tree analysis, methods, and applications—a review. IEEE Reliab Trans R-34:194–203
Li N (2011) “Research of the calculation method structure system fuzzy reliability based on the fault tree”. Adv Mater Res 201–203:968–973
Liang G, Wang M (1993) Fuzzy fault-tree analysis using failure possibility. Microelectron Reliab 33(4):583–597
Lin CT, Wang MJ (1997) Hybrid fault tree analysis using fuzzy sets. Reliab Eng Syst Saf 58:205–213
M Al Humaidi (2010) A fuzzy logic approach to model delays in construction projects using rotational fuzzy fault tree models. Civ Eng Environ Syst 27(4):329–351
Ulieru M From fault trees to fuzzy relations in managing heuristics for technical diagnosis. In: The IEEE International conference on systems, man and cybernetics. part 2 (of 5), Le Touquet, Fr, 10/17–20/93 ol. 21993
Mentes A (2011) An application of fuzzy fault tree analysis for spread mooring systems. Ocean Eng 38(2–3):285–294
Misra kB, Soman kP (1995) “Multistate fault tree analysis using fuzzy probability vectors and resolution identity” reliability and safety analysis under fuzziness. Physica, Heidelberg
Misra KB, Weber GG (1989) A new method for fuzzy fault tree analysis. Microelectron Reliab 29:195
Mohan S, Elango K and Sivakumar S (2003) Evaluation of risk in canal irrigation systems due to non-maintenance using fuzzy fault tree approach. In: Presented at INDIN 2003 proceedings IEEE international conference on industrial informatics, 2003
Mokhtari K (2011) Application of a generic bow-tie based risk analysis framework on risk management of sea ports and offshore terminals. J Hazard Mater 192(2):465–475
NASA Office of Safety and Mission Assurance (2002) Fault tree handbook with aerospace applications. NASA Headquarters, Washington DC
Onisawa T (1988) An approach to human reliability in man-machine systems using error possibility. Fuzzy Sets Syst 27(2):87–103
Page LB, Perry JE (1994) Standard deviation as an alternative to fuzziness in fault tree models. IEEE Trans Reliab 43(3):402
Pan H, Huang J and Liu G (2008) Fault diagnosis of circuit board based on fault tree. In: Presented at 10th international conference on control, automation, robotics and vision, ICARCV 2008
Pan N and Wang H (2007) Assessing failure of bridge construction using fuzzy fault tree analysis. In: Fourth international conference on fuzzy systems and knowledge discovery, p 96–100
Pan H, Yun W (1997) Fault tree analysis with fuzzy gates. Comput Ind Eng 33(3–4):569–572
Peng Z, Xiaodong M, Zongrun Y and Zhaoxiang Y (2008) An approach of fault diagnosis for system based on fuzzy fault tree. In: International conference on multimedia and information technology, MMIT ‘08, 2008; p 697–700
Purba JH (2010) A hybrid approach for fault tree analysis combining probabilistic method with fuzzy numbers” lecture notes in computer science, vol 6113 LNAI(PART 1)., pp 194–201
Rasmussen NC (1975) Reactor safety study. WASH-1400, US Nuclear Regulatory Commission
Renjith V, Madhua G, Nayagam V, Bhasi AB (2010) Two-dimensional fuzzy fault tree analysis for chlorine release from a chlor-alkali industry using expert elicitation. J Hazard Mater 183(1–3):103–110
Rong W and Xin D (2010) Application of fuzzy fault tree analysis on burning and blasting of LPG tank. In: International conference on logistics systems and intelligent management, p 1093–1096
Sawyer JP (1994) Fault tree analysis of fuzzy mechanical systems. Microelectron Reliab 34(4):653–667
Sharma U (1993) Use of recursive methods in fuzzy fault tree analysis: an aid to quantitative risk analysis. Reliab Eng Syst Saf 41(3):231–237
Shengwu H and Xiaosan G (2010) GIS reliability analysis based trapezoid fuzzy fault tree, In: 18th international conference on geoinformatics, 2010; p 1–5
Shu M, Cheng C, Chang J (2006) Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly. Microelectron Reliab 46(12):2139–2148
Singer D (1990) A fuzzy set approach to fault tree and reliability analysis. Fuzzy Sets Syst 34(2):145
Siontorou CG (2008) Carbohydrate detection failure analysis via biosensoring. IEEE Trans Instrum Meas 57(12):2856–2867
Siontorou CG (2011) Error identification/propagation/remediation in biomonitoring surveys—a knowledge-based approach towards standardization via fault tree analysis. Ecol Ind 11(2):564–581
Song H (2009) Fuzzy fault tree analysis based on T-S model with application to INS/GPS navigation system. Soft Comput 13(1):31–40
Sui Y (2011) Reliability assessment of urban anti-disasters system based on fuzzy fault tree analysis In: 2nd IEEE international conference on emergency management and management sciences (ICEMMS), p 159–162
Suresh P, Babar A, Raj V (1996) Uncertainty in fault tree analysis: a fuzzy approach. Fuzzy Sets Syst 83(2):135–141
Szabó S, Németh B and Kiss I (2010) Eliminating bias in fuzzy fault trees for electrostatic risk assessment. In: Presented at 4th international workshop on soft computing applications (SOFA), 2010
Szabo SV, Kiss I, Ne´meth B, Berta I (2011) Complex system for risk assessment in ESD hazardous processes with unusually high risks. J Phys Conf Ser 301(1):012037
Tanaka H, Fan LT, Lai FS, Toguchi K (1983) Fault tree analysis by fuzzy probability. IEEE Reliab Trans 32:453–457
Tong W, Guangyu T, Bo ZQ and Klimek A (2007) Fuzzy set theory and fault tree analysis based method suitable for fault diagnosis of power transformer. In: International conference on intelligent systems applications to power systems, ISAP 2007; p 1–5
Tong W, Guangyu T, Yi L and Jun W (2007) Study on power transformer fault diagnosis method based on fuzzy tree. In: Presented at international power engineering conference, IPEC 2007
Ulieru M (1994) Diagnosis by approximate reasoning on dynamic fuzzy fault trees. In: Presented at IEEE world congress on computational intelligence proceedings of the third IEEE conference on fuzzy systems, 1994
Wang YF (2011) Quantitative risk analysis model of integrating fuzzy fault tree with bayesian network. In: IEEE international conference on intelligence and security informatics (ISI), 2011; p 267–271
Wang Y (2011) Quantitative risk assessment through hybrid causal logic approach. Proc Insti Mech Eng. 2011; 225(3): 323–332.
Wang F and Wu D (2007) Design and implementation of a missile fault diagnosis system based on fault-tree analysis. In: Presented at machine international conference on learning and cybernetics, 2007
Wang Y, Li L, Chang M, Chen H, Dong X, Ren Y, Li Q and Liu D (2009) Fault diagnosis expert system based on integration of fault-tree and neural network. In: Presented at international conference on computational intelligence and software engineering, CiSE 2009
Weber DP (1994a) Fuzzy weibull for risk analysis” reliability and maintainability symposium, proceedings, annual, 1994; p 456–461
Wei L, Yanjiao J and Yumin S (2009) Assessment of grid construction project risk of process based on fuzzy-event tree-fault tree.In: Presented at international symposium on information engineering and electronic commerce, IEEC ‘09, 2009
Xiao-lin L, Yan-xia Z and Zeng-hui Z, (2010) Research on application of fuzzy fault tree analysis in the electronic equipment fault diagnosis. In: The 2nd international conference on computer and automation engineering (ICCAE), 2010; p 65–67
Xie G (2010) Fault diagnosis platform for radar circuit based on virtual instrument. In: International conference on measuring technology and mechatronics automation (ICMTMA), p 245–248
Yao Z (2010) “Design of coal-mechanical online fault diagnosis based embedded system” Informatics in Control. Autom Robot (CAR) 1:178–181
Yao Z (2011) Fault diagnosis analysis of gear based on fuzzy fault tree. Adv Mater Res 204–210:1994–1997
Yao C and Zhang Y (2010) T–S model based fault tree analysis on the hoisting system of rubber-tyred girder hoister. In: Presented at WASE international conference on information engineering (ICIE), 2010
Yao Z, Lou G, Song X and Zhou Y (2010) On-line fault diagnosis study for roller bearing based on fuzzy fault tree. In: Presented at 2nd international Asia conference on informatics in control, automation and robotics (CAR), 2010
Yi R and Leixing K (2011) Fuzzy multi-state fault tree analysis based on fuzzy expert system. In: Presented at 9th international conference on reliability, maintainability and safety (ICRMS), 2011
Yuliang C and Tiejun Z (2010) Research on the application of fuzzy fault tree analysis method in the machinery equipment fault diagnosis. In: 2nd international Asia conference on informatics in control, automation and robotics (CAR), 2010; p 84–87
Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 1:28
Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1(1):3
Zadeh LA (1988) Fuzzy logic. Computer 21(4):83–93
Zhang X (2010) “Engineering machinery engine system reliability analysis”. Adv Mater Res 139–141:2587–2590
Zhang Z, Wang Z and Zhang B (2008) Studies on median value of fuzzy numbers based on confidence level. In: Presented at international conference on machine learning and cybernetics, 2008
Zhu DQ, Yu SL (2002) Survey of knowledge—based fault diagnosis methods. J Anhui Univ Technol 19(3):197
Zimmerman H (1983) Using fuzzy sets in operational research. Eur J Oper Res 13(3):201–216
Zong-Xiao Y, Suzuki K, Shimada Y and Sayama H (1995) Fuzzy fault diagnostic system based on fault tree analysis. Proceedings of 1995 IEEE international conference on international fuzzy engineering symposium, 1995; vol 1: p 165–170
Zonouz SA and Miremadi SG (2006) A fuzzy-monte carlo simulation approach for fault tree analysis. In: Reliability and maintainability symposium, RAMS 06, 2006
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mahmood, Y.A., Ahmadi, A., Verma, A.K. et al. Fuzzy fault tree analysis: a review of concept and application. Int J Syst Assur Eng Manag 4, 19–32 (2013). https://doi.org/10.1007/s13198-013-0145-x
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-013-0145-x