Abstract
In recent decades the dependency of a society on the industrial sector has been widely increased, leading to the rapid occurrence of high number of industrial accidents. Thus, it is important to be ensured that the operations of their complex systems and commonly hazardous components are still safe working. The probabilistic failure analysis is commonly engaged to improve the safety performance of the system using varieties of methods including fault tree analysis, bow-tie analysis, or block diagram analysis. Such mentioned methods in some cases can be applied with consideration of multi-expert judgment which brings high subjectivity its inside. The current work is aimed at performing a subjective probabilistic failure analysis by considering rational consensus in the industrial sector. Therefore, Bayesian network method is utilized in this regards and accordingly the validation of this technique is evaluated based on the literature and real industrial case study.
Similar content being viewed by others
References
Abimbola M, Khan F, Khakzad N (2014) Dynamic safety risk analysis of offshore drilling. J Loss Prev Process Ind 30:74–85. https://doi.org/10.1016/j.jlp.2014.05.002
Abimbola M, Khan F, Khakzad N, Butt S (2015) Safety and risk analysis of managed pressure drilling operation using Bayesian network. Saf Sci 76:133–144. https://doi.org/10.1016/j.ssci.2015.01.010
Adedigba SA, Khan F, Yang M (2016) Dynamic safety analysis of process systems using nonlinear and non-sequential accident model. Chem Eng Res Des 111:169–183. https://doi.org/10.1016/j.cherd.2016.04.013
Afenyo M, Khan F, Veitch B, Yang M (2017) Arctic shipping accident scenario analysis using Bayesian network approach. Ocean Eng 133:224–230. https://doi.org/10.1016/j.oceaneng.2017.02.002
Amin MT, Imtiaz S, Khan F (2018) Process system fault detection and diagnosis using a hybrid technique. Chem Eng Sci 189:191–211. https://doi.org/10.1016/j.ces.2018.05.045
Aven T, Krohn BS (2014) A new perspective on how to understand, assess and manage risk and the unforeseen. Reliab Eng Syst Saf 121:1–10. https://doi.org/10.1016/J.RESS.2013.07.005
Aven T, Zio E (2011) Some considerations on the treatment of uncertainties in risk assessment for practical decision making. Reliab Eng Syst Saf 96:64–74. https://doi.org/10.1016/J.RESS.2010.06.001
Aven T, Zio E (2014) Foundational issues in risk assessment and risk management. Risk Anal 34:1164–1172. https://doi.org/10.1111/risa.12132
Awasthi A, Chauhan SS (2011) Using AHP and Dempster–Shafer theory for evaluating sustainable transport solutions. Environ Model Softw 26:787–796. https://doi.org/10.1016/j.envsoft.2010.11.010
Ayyub B (2001) A practical guide on conducting expert-opinion elicitation of probabilities and consequences for corps facilities. Institute for Water Resources, Alexandria
Bae H-R, Grandhi RV, Canfield RA (2004) An approximation approach for uncertainty quantification using evidence theory. Reliab Eng Syst Saf 86:215–225. https://doi.org/10.1016/j.ress.2004.01.011
Baksh AA, Khan F, Gadag V, Ferdous R (2015) Network based approach for predictive accident modelling. Saf Sci 80:274–287. https://doi.org/10.1016/j.ssci.2015.08.003
Bari RA, Park CK (1989) Uncertainty characterization of data for probabilistic risk assessment. Reliab Eng Syst Saf 26:163–172. https://doi.org/10.1016/0951-8320(89)90072-0
Barua S, Gao X, Pasman H, Mannan MS (2016) Bayesian network based dynamic operational risk assessment. J Loss Prev Process Ind 41:399–410. https://doi.org/10.1016/j.jlp.2015.11.024
Bedford T, Cooke RM (2001) Probabilistic risk analysis: foundations and methods. Cambridge University Press, Cambridge. http://www.cambridge.org/gb/academic/subjects/statistics-probability/optimization-or-and-risk/probabilistic-risk-analysis-foundations-and-methods?format=HB&isbn=9780521773201#6t6V72jqRJTtdYUa.97. Accessed 31 Mar 2018
Bhandari J, Abbassi R, Garaniya V, Khan F (2015) Risk analysis of deepwater drilling operations using Bayesian network. J Loss Prev Process Ind 38:11–23. https://doi.org/10.1016/J.JLP.2015.08.004
Bhandari J, Arzaghi E, Abbassi R, Garaniya V, Khan F (2016) Dynamic risk-based maintenance for offshore processing facility. Process Saf Prog 35:399–406. https://doi.org/10.1002/prs.11829
Bobbio A, Portinale L, Minichino M, Ciancamerla E (2001) Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliab Eng Syst Saf 71:249–260. https://doi.org/10.1016/S0951-8320(00)00077-6
Bouejla A, Chaze X, Guarnieri F, Napoli A (2014) A Bayesian network to manage risks of maritime piracy against offshore oil fields. Saf Sci 68:222–230. https://doi.org/10.1016/j.ssci.2014.04.010
Catrinu MD, Nordgård DE (2011) Integrating risk analysis and multi-criteria decision support under uncertainty in electricity distribution system asset management. Reliab Eng Syst Saf 96:663–670. https://doi.org/10.1016/J.RESS.2010.12.028
Celik M, Lavasani SM, Wang J (2010) A risk-based modelling approach to enhance shipping accident investigation. Saf Sci 48:18–27. https://doi.org/10.1016/j.ssci.2009.04.007
Chan F, Kumar N, Tiwari M, Lau H (2008) Global supplier selection: a fuzzy-AHP approach. Int J Prod Res. https://doi.org/10.1080/00207540600787200
Cheok MC, Parry GW, Sherry RR (1998) Use of importance measures in risk-informed regulatory applications. Reliab Eng Syst Saf 60:213–226. https://doi.org/10.1016/S0951-8320(97)00144-0
Chiremsel Z, Nait Said R, Chiremsel R (2016) Probabilistic fault diagnosis of safety instrumented systems based on fault tree analysis and Bayesian network. J Fail Anal Prev 16:747–760. https://doi.org/10.1007/s11668-016-0140-z
Colson AR, Cooke RM (2017) Cross validation for the classical model of structured expert judgment. Reliab Eng Syst Saf 163:109–120. https://doi.org/10.1016/J.RESS.2017.02.003
Cooke R (1991) Experts in uncertainty: opinion and subjective probability in science. Oxford University Press, Oxford. https://books.google.pt/books/about/Experts_in_Uncertainty.html?id=4taZBr_nvBgC&redir_esc=y. Accessed 30 Mar 2018
Cooke R (2010) Conundrums with uncertainty factors: perspective. Risk Anal 30:330–339. https://doi.org/10.1111/j.1539-6924.2009.01336.x
Cooke RM (2014) Validating expert judgment with the classical model. Springer, Cham, pp 191–212. https://doi.org/10.1007/978-3-319-08551-7_10
Cooke RM (2018) Validation in the classical model. Springer, Cham, pp 37–59. https://doi.org/10.1007/978-3-319-65052-4_3
Cooke RM, Goossens LHJ (2004) Expert judgement elicitation for risk assessments of critical infrastructures. J Risk Res 7:643–656. https://doi.org/10.1080/1366987042000192237
Cooke RM, Goossens LLHJ (2008) TU Delft expert judgment data base. Reliab Eng Syst Saf 93:657–674. https://doi.org/10.1016/j.ress.2007.03.005
Cooke RM, ElSaadany S, Huang X (2008) On the performance of social network and likelihood-based expert weighting schemes. Reliab Eng Syst Saf 93:745–756. https://doi.org/10.1016/J.RESS.2007.03.017
Cooke RM, Johnson RW, Dame N (2016) Supplementary online material for cross validation of classical model for structured expert judgment 1–35
Curcur G, Galante GM, La Fata CM (2012) Epistemic uncertainty in fault tree analysis approached by the evidence theory. J Loss Prev Process Ind 25:667–676. https://doi.org/10.1016/j.jlp.2012.02.003
Deng X, Hu Y, Deng Y, Mahadevan S (2014) Supplier selection using AHP methodology extended by D numbers. Expert Syst Appl 41:156–167. https://doi.org/10.1016/j.eswa.2013.07.018
Di Bona G, Silvestri A, Forcina A, Falcone D (2017) AHP-IFM target: an innovative method to define reliability target in an aerospace prototype based on analytic hierarchy process. Qual Reliab Eng Int 33:1731–1751. https://doi.org/10.1002/qre.2140
Dugan JB, Bavuso SJ, Boyd MA (1993) Fault trees and Markov models for reliability analysis of fault-tolerant digital systems. Reliab Eng Syst Saf 39:291–307
Duru O, Bulut E, Yoshida S (2012) Regime switching fuzzy AHP model for choice-varying priorities problem and expert consistency prioritization: a cubic fuzzy-priority matrix design. Expert Syst Appl 39:4954–4964. https://doi.org/10.1016/j.eswa.2011.10.020
El-Gheriani M, Khan F, Zuo MJ (2017) Rare event analysis considering data and model uncertainty. ASCE ASME J Risk Uncertain Eng Syst Part B Mech Eng 3:021008. https://doi.org/10.1115/1.4036155
Escande J, Proust C, Le Coze JC (2016) Limitations of current risk assessment methods to foresee emerging risks: Towards a new methodology? J Loss Prev Process Ind 43:730–735. https://doi.org/10.1016/j.jlp.2016.06.008
Ferdous R, Khan F, Sadiq R, Amyotte P, Veitch B (2011) Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations. Risk Anal 31:86–107. https://doi.org/10.1111/j.1539-6924.2010.01475.x
Ferdous R, Khan F, Sadiq R, Amyotte P, Veitch B (2013) Analyzing system safety and risks under uncertainty using a bow-tie diagram: an innovative approach. Process Saf Environ Prot 91:1–18. https://doi.org/10.1016/j.psep.2011.08.010
Garrick BJ (1988) The approach to risk analysis in three industries: nuclear power, space systems, and chemical process. Reliab Eng Syst Saf 23:195–205. https://doi.org/10.1016/0951-8320(88)90109-3
Gharahbagheri H, Imtiaz SA, Khan F (2017) Root cause diagnosis of process fault using KPCA and Bayesian network. Ind Eng Chem Res 56:2054–2070. https://doi.org/10.1021/acs.iecr.6b01916
Golinescu RP, Morosan F, Kazimi MS (1997) A probabilistic methodology for the design of radiological confinement of tokamak reactors. Reliab Eng Syst Saf 58:275–296
Gul M (2018) A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions. Hum Ecol Risk Assess Int J 24:1–38. https://doi.org/10.1080/10807039.2018.1424531
Gul M, Guneri AF (2016) A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry. J Loss Prev Process Ind 40:89–100. https://doi.org/10.1016/j.jlp.2015.11.023
Hänninen M (2014) Bayesian networks for maritime traffic accident prevention: benefits and challenges. Accid Anal Prev 73:305–312. https://doi.org/10.1016/j.aap.2014.09.017
Hashemi SJ, Khan F, Ahmed S (2016) Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian network model. Comput Chem Eng 93:128–142. https://doi.org/10.1016/j.compchemeng.2016.06.011
Hong Y, Pasman HJ, Sachdeva S, Markowski AS, Mannan MS (2016) A fuzzy logic and probabilistic hybrid approach to quantify the uncertainty in layer of protection analysis. J Loss Prev Process Ind 43:10–17. https://doi.org/10.1016/j.jlp.2016.04.006
IOOC (2012) Learning from accident, Kharg Island
IOOC (2015) Introduction to Iranian offshore oil company (IOOC), Kharg Island
Ishikawa A, Amagasa M, Shiga T, Tomizawa G, Tatsuta R, Mieno H (1993) The max–min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets Syst 55:241–253. https://doi.org/10.1016/0165-0114(93)90251-C
Javadi M, Saeedi G, Shahriar K (2017) Developing a new probabilistic approach for risk analysis, application in underground coal mining. J Fail Anal Prev 17:989–1010. https://doi.org/10.1007/s11668-017-0325-0
Jensen U (2002) Probabilistic risk analysis: foundations and methods. J Am Stat Assoc. https://doi.org/10.1198/016214502760301264
Jensen FV, Nielsen TD (2007) Bayesian networks and decision graphs. Springer, New York. https://doi.org/10.1007/978-0-387-68282-2
Ji J, Tong Q, Khan F, Dadashzadeh M, Abbassi R (2018) Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities. Ind Eng Chem Res 57:3990–4006. https://doi.org/10.1021/acs.iecr.8b00103
Johansen IL, Rausand M (2015) Ambiguity in risk assessment. Saf Sci 80:243–251. https://doi.org/10.1016/j.ssci.2015.07.028
Kabir G, Hasin MAA (2012) Integrating modified Delphi method with fuzzy AHP for optimal power substation location selection. Int J Multicriteria Decis Mak. https://doi.org/10.1504/ijmcdm.2013.056654
Kabir S, Papadopoulos Y (2018) A review of applications of fuzzy sets to safety and reliability engineering. Int J Approx Reason 100:29–55. https://doi.org/10.1016/j.ijar.2018.05.005
Kabir G, Sadiq R, Tesfamariam S (2015) A fuzzy Bayesian belief network for safety assessment of oil and gas pipelines. Struct Infrastruct Eng 2479:1–16. https://doi.org/10.1080/15732479.2015.1053093
Kabir S, Walker M, Papadopoulos Y (2018a) Dynamic system safety analysis in HiP-HOPS with Petri nets and Bayesian networks. Saf Sci 105:55–70. https://doi.org/10.1016/j.ssci.2018.02.001
Kabir S, Yazdi M, Aizpurua JI, Papadopoulos Y (2018b) Uncertainty-aware dynamic reliability analysis framework for complex systems. IEEE Access 6:29499–29515. https://doi.org/10.1109/ACCESS.2018.2843166
Kalantarnia M, Khan F, Hawboldt K (2010) Modelling of BP Texas city refinery accident using dynamic risk assessment approach. Process Saf Environ Prot 88:191–199. https://doi.org/10.1016/j.psep.2010.01.004
Kaplan S (1997) The words of risk analysis. Risk Anal 17:407–417. https://doi.org/10.1111/j.1539-6924.1997.tb00881.x
Kelly DL, Smith CL (2009) Bayesian inference in probabilistic risk assessment—the current state of the art. Reliab Eng Syst Saf 94:628–643. https://doi.org/10.1016/j.ress.2008.07.002
Khakzad N (2015) Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliab Eng Syst Saf 138:263–272. https://doi.org/10.1016/j.ress.2015.02.007
Khakzad N, Khan F, Amyotte P (2011) Safety analysis in process facilities: comparison of fault tree and Bayesian network approaches. Reliab Eng Syst Saf 96:925–932. https://doi.org/10.1016/j.ress.2011.03.012
Khakzad N, Khan F, Amyotte P (2012) Dynamic risk analysis using bow-tie approach. Reliab Eng Syst Saf 104:36–44. https://doi.org/10.1016/j.ress.2012.04.003
Khakzad N, Khan F, Amyotte P (2013a) Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Saf Environ Prot 91:46–53. https://doi.org/10.1016/j.psep.2012.01.005
Khakzad N, Khan F, Amyotte P (2013b) Quantitative risk analysis of offshore drilling operations: a Bayesian approach. Saf Sci 57:108–117. https://doi.org/10.1016/j.ssci.2013.01.022
Khakzad N, Khan F, Amyotte P, Cozzani V (2013c) Domino effect analysis using Bayesian networks. Risk Anal 33:292–306. https://doi.org/10.1111/j.1539-6924.2012.01854.x
Khakzad N, Khan F, Amyotte P (2013d) Risk-based design of process systems using discrete-time Bayesian networks. Reliab Eng Syst Saf 109:5–17. https://doi.org/10.1016/j.ress.2012.07.009
Khan F, Rathnayaka S, Ahmed S (2015) Methods and models in process safety and risk management: past, present and future. Process Saf Environ Prot 98:116–147. https://doi.org/10.1016/j.psep.2015.07.005
Kharat MG, Kamble SJ, Raut RD, Kamble SS (2016) Identification and evaluation of landfill site selection criteria using a hybrid fuzzy Delphi, fuzzy AHP and DEMATEL based approach. Model Earth Syst Environ 2:98. https://doi.org/10.1007/s40808-016-0171-1
Kutlu AC, Ekmekçioǧlu M (2012) Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst Appl 39:61–67. https://doi.org/10.1016/j.eswa.2011.06.044
Lavasani SM, Ramzali N, Sabzalipour F, Akyuz E (2015a) Utilisation of fuzzy fault tree analysis (FFTA) for quantified risk analysis of leakage in abandoned oil and natural-gas wells. Ocean Eng 108:729–737. https://doi.org/10.1016/j.oceaneng.2015.09.008
Lavasani SM, Zendegani A, Celik M (2015b) An extension to fuzzy fault tree analysis (FFTA) application in petrochemical process industry. Process Saf Environ Prot 93:75–88. https://doi.org/10.1016/j.psep.2014.05.001
Li J, Hale A (2016) Output distributions and topic maps of safety related journals. Saf Sci 82:236–244. https://doi.org/10.1016/j.ssci.2015.09.004
Li X, Chen G, Jiang S, He R, Xu C, Zhu H (2018) Developing a dynamic model for risk analysis under uncertainty: case of third-party damage on subsea pipelines. J Loss Prev Process Ind 54:289–302. https://doi.org/10.1016/J.JLP.2018.05.001
Lin C-T, Wang M-JJ (1997) Hybrid fault tree analysis using fuzzy sets fFL (X). Reliab Eng Syst Saf 58:205–213. https://doi.org/10.1016/S0951-8320(97)00072-0
Liu TS, Chiou SB (1997) The application of Petri nets to failure analysis. Reliab Eng Syst Saf 57:129–142. https://doi.org/10.1016/S0951-8320(97)00030-6
Markowski AS, Mannan MS, Bigoszewska A (2009) Fuzzy logic for process safety analysis. J Loss Prev Process Ind 22:695–702. https://doi.org/10.1016/j.jlp.2008.11.011
Mbakwe AC, Saka AA, Choi K, Lee Y-J (2016) Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network. Accid Anal Prev 93:135–146. https://doi.org/10.1016/j.aap.2016.04.020
Minatour Y, Bonakdari H, Aliakbarkhani ZS (2016) Extension of fuzzy Delphi AHP based on interval-valued fuzzy sets and its application in water resource rating problems. Water Resour Manag 30:3123–3141. https://doi.org/10.1007/s11269-016-1335-5
Murphy F, Sheehan B, Mullins M, Bouwmeester H, Marvin HJP, Bouzembrak Y, Costa AL, Das R, Stone V, Tofail SAM (2016) A tractable method for measuring nanomaterial risk using Bayesian networks. Nanoscale Res Lett 11:503. https://doi.org/10.1186/s11671-016-1724-y
Nedjati A, Vizvari B, Izbirak G (2016) Post-earthquake response by small UAV helicopters. Nat Hazards 80:1669–1688. https://doi.org/10.1007/s11069-015-2046-6
Nedjati A, Izbirak G, Arkat J (2017) Bi-objective covering tour location routing problem with replenishment at intermediate depots: formulation and meta-heuristics. Comput Ind Eng 110:191–206. https://doi.org/10.1016/J.CIE.2017.06.004
Øien K (2001) A framework for the establishment of organizational risk indicators. Reliab Eng Syst Saf 74:147–167. https://doi.org/10.1016/S0951-8320(01)00068-0
Omidvari M, Lavasani SMRR, Mirza S (2014) Presenting of failure probability assessment pattern by FTA in Fuzzy logic (case study: distillation tower unit of oil refinery process). J Chem Health Saf 21:14–22. https://doi.org/10.1016/j.jchas.2014.06.003
Pars Oil and Gas Company (2016) Fire and explosion accident report (Isobutane storage tank-2016), Asaluyeh
Paté-Cornell ME (1996) Uncertainties in risk analysis: six levels of treatment. Reliab Eng Syst Saf 54:95–111. https://doi.org/10.1016/S0951-8320(96)00067-1
Ping P, Wang K, Kong D, Chen G (2018) Estimating probability of success of escape, evacuation, and rescue (EER) on the offshore platform by integrating Bayesian network and fuzzy AHP. J Loss Prev Process Ind 54:57–68. https://doi.org/10.1016/j.jlp.2018.02.007
Quigley J, Colson A, Aspinall W, Cooke RM (2018) Elicitation in the classical model. Springer, Cham, pp 15–36. https://doi.org/10.1007/978-3-319-65052-4_2
Ramzali N, Lavasani MRM, Ghodousi J (2015) Safety barriers analysis of offshore drilling system by employing fuzzy event tree analysis. Saf Sci 78:49–59. https://doi.org/10.1016/j.ssci.2015.04.004
Rathnayaka S, Khan F, Amyotte P (2011) SHIPP methodology: predictive accident modeling approach. Part I: methodology and model description. Process Saf Environ Prot 89:151–164. https://doi.org/10.1016/j.psep.2011.01.002
Rausand M, Hoyland A (2004) System reliability theory: models, statistical methods, and applications. Wiley, Hoboken, p 664. https://doi.org/10.1109/wescon.1996.554026
Raviv G, Shapira A, Fishbain B (2017) AHP-based analysis of the risk potential of safety incidents: case study of cranes in the construction industry. Saf Sci 91:298–309. https://doi.org/10.1016/j.ssci.2016.08.027
Reid SG (2009) Confidence and risk. Struct Saf 31:98–104. https://doi.org/10.1016/j.strusafe.2008.06.006
Reniers G, Anthone Y (2012) A ranking of safety journals using different measurement methods. Saf Sci 50:1445–1451. https://doi.org/10.1016/j.ssci.2012.01.017
Reniers GLL, Dullaert W, Ale BJM, Soudan K (2005) The use of current risk analysis tools evaluated towards preventing external domino accidents. J Loss Prev Process Ind 18:119–126. https://doi.org/10.1016/j.jlp.2005.03.001
Renn O (1998) The role of risk perception for risk management. Reliab Eng Syst Saf 59:49–62. https://doi.org/10.1016/S0951-8320(97)00119-1
Sengupta A, Bandyopadhyay D, Van Westen CJ, Van Der Veen A (2016) An evaluation of risk assessment framework for industrial accidents in India. J Loss Prev Process Ind 41:295–302. https://doi.org/10.1016/j.jlp.2015.12.012
Song X, Zhai Z, Zhu P, Han J (2017a) A stochastic computational approach for the analysis of fuzzy systems. IEEE Access 5:13465–13477. https://doi.org/10.1109/ACCESS.2017.2728123
Song G, Khan F, Yang M, Wang H (2017b) Predictive abnormal events analysis using continuous bayesian network. ASCE ASME J Risk Uncertain Eng Sys Part B Mech Eng 3:041004. https://doi.org/10.1115/1.4035438
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 64:583–616. https://doi.org/10.1111/1467-9868.00353
Sule I, Khan F, Butt S, Yang M (2018) Kick control reliability analysis of managed pressure drilling operation. J Loss Prev Process Ind 52:7–20. https://doi.org/10.1016/j.jlp.2018.01.007
Taveau J (2010) Risk assessment and land-use planning regulations in France following the AZF disaster. J Loss Prev Process Ind 23:813–823. https://doi.org/10.1016/j.jlp.2010.04.003
Walker ID, Cavallaro JR (1996) Failure mode analysis for a hazardous waste clean-up manipulator. Reliab Eng Syst Saf 53:277–290. https://doi.org/10.1016/S0951-8320(96)00055-5
Wang J, Huang H (2016) Road network safety evaluation using Bayesian hierarchical joint model. Accid Anal Prev 90:152–158. https://doi.org/10.1016/j.aap.2016.02.018
Wang J, Yang JB, Sen P (1995) Safety analysis and synthesis using fuzzy sets and evidential reasoning. Reliab Eng Syst Saf 47:103–118. https://doi.org/10.1016/0951-8320(94)00053-Q
Wang Y-M, Luo Y, Hua Z (2008) On the extent analysis method for fuzzy AHP and its applications. Eur J Oper Res 186:735–747. https://doi.org/10.1016/J.EJOR.2007.01.050
Winkler RL (1996) Uncertainty in probabilistic risk assessment. Reliab Eng Syst Saf 54:127–132. https://doi.org/10.1016/S0951-8320(96)00070-1
Wu J, Huang HB, Cao QW (2013) Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multi-criteria decision making problems. Appl Math Model 37:9898–9906. https://doi.org/10.1016/j.apm.2013.05.035
Wu W-S, Yang C-F, Chang J-C, Château P-A, Chang Y-C (2015) Risk assessment by integrating interpretive structural modeling and Bayesian network, case of offshore pipeline project. Reliab Eng Syst Saf 142:515–524. https://doi.org/10.1016/j.ress.2015.06.013
Yan F, Xu K, Yao X, Li Y (2016) Fuzzy bayesian network-bow-tie analysis of gas leakage during biomass gasification. PLoS ONE 11:e0160045. https://doi.org/10.1371/journal.pone.0160045
Yazdi M (2017a) The application of bow-tie method in hydrogen sulfide risk management using layer of protection analysis (LOPA). J Fail Anal Prev 17:291–303. https://doi.org/10.1007/s11668-017-0247-x
Yazdi M (2017b) Hybrid probabilistic risk assessment using fuzzy FTA and fuzzy AHP in a process industry. J Fail Anal Prev 17:756–764. https://doi.org/10.1007/s11668-017-0305-4
Yazdi M (2017c) An extension of fuzzy improved risk graph and fuzzy analytical hierarchy process for determination of chemical complex safety integrity levels. Int J Occup Saf Ergon. https://doi.org/10.1080/10803548.2017.1419654
Yazdi M (2018a) Risk assessment based on novel intuitionistic fuzzy-hybrid-modified TOPSIS approach. Saf Sci 110:438–448. https://doi.org/10.1016/j.ssci.2018.03.005
Yazdi M (2018b) Improving failure mode and effect analysis (FMEA) with consideration of uncertainty handling as an interactive approach. Int J Interact Des Manuf. https://doi.org/10.1007/s12008-018-0496-2
Yazdi M (2018c) Footprint of knowledge acquisition improvement in failure diagnosis analysis. Qual Reliab Eng Int. https://doi.org/10.1002/qre.2408
Yazdi M, Kabir S (2017) A fuzzy Bayesian network approach for risk analysis in process industries. Process Saf Environ Prot 111:507–519. https://doi.org/10.1016/j.psep.2017.08.015
Yazdi M, Kabir S (2018) Fuzzy evidence theory and Bayesian networks for process systems risk analysis. Hum Ecol Risk Assess An Int J 1–30. https://doi.org/10.1080/10807039.2018.1493679
Yazdi M, Soltanali H (2018) Knowledge acquisition development in failure diagnosis analysis as an interactive approach. Int J Interact Des Manuf 20:18. https://doi.org/10.1007/s12008-018-0504-6
Yazdi M, Zarei E (2018) Uncertainty handling in the safety risk analysis: an integrated approach based on fuzzy fault tree analysis. J Fail Anal Prev. https://doi.org/10.1007/s11668-018-0421-9
Yazdi M, Darvishmotevali M (2019) Fuzzy-based failure diagnostic analysis in a chemical process industry. Springer, Cham, pp. 724–731. https://doi.org/10.1007/978-3-030-04164-9_95
Yazdi M, Nikfar F, Nasrabadi M (2017a) Failure probability analysis by employing fuzzy fault tree analysis. Int J Syst Assur Eng Manag 8:1177–1193. https://doi.org/10.1007/s13198-017-0583-y
Yazdi M, Daneshvar S, Setareh H (2017b) An extension to fuzzy developed failure mode and effects analysis (FDFMEA) application for aircraft landing system. Saf Sci 98:113–123. https://doi.org/10.1016/j.ssci.2017.06.009
Yazdi M, Korhan O, Daneshvar S (2018) Application of fuzzy fault tree analysis based on modified fuzzy AHP and fuzzy TOPSIS for fire and explosion in process industry. Int J Occup Saf Ergon 0:1–18. https://doi.org/10.1080/10803548.2018.1454636
Yu YCT, Lee PTC (2014) Erratum to: a fuzzy AHP approach to construct international hotel spa atmosphere evaluation model. Qual Quant. https://doi.org/10.1007/s11135-014-0028-5
Yuan Z, Khakzad N, Khan F, Amyotte P (2015a) Risk analysis of dust explosion scenarios using bayesian networks. Risk Anal 35:278–291. https://doi.org/10.1111/risa.12283
Yuan Z, Khakzad N, Khan F, Amyotte P (2015b) Risk-based optimal safety measure allocation for dust explosions. Saf Sci 74:79–92. https://doi.org/10.1016/j.ssci.2014.12.002
Zarei E, Azadeh A, Khakzad N, Aliabadi MM, Mohammadfam I (2017a) Dynamic safety assessment of natural gas stations using Bayesian network. J Hazard Mater 321:830–840. https://doi.org/10.1016/j.jhazmat.2016.09.074
Zarei E, Azadeh A, Aliabadi MM, Mohammadfam I (2017b) Dynamic safety risk modeling of process systems using bayesian network. Process Saf Prog. https://doi.org/10.1002/prs.11889
Zerrouki H, Smadi H (2017) Bayesian belief network used in the chemical and process industry: a review and application. J Fail Anal Prev 17:159–165. https://doi.org/10.1007/s11668-016-0231-x
Zerrouki H, Tamrabet A (2015) Safety and risk analysis of an operational heater using Bayesian network. J Fail Anal Prev 15:657–661. https://doi.org/10.1007/s11668-015-9986-8
Zhang L, Wu X, Skibniewski MJ, Zhong J, Lu Y (2014) Bayesian-network-based safety risk analysis in construction projects. Reliab Eng Syst Saf 131:29–39. https://doi.org/10.1016/j.ress.2014.06.006
Zhang G, Thai VV, Yuen KF, Loh HS, Zhou Q (2018) Addressing the epistemic uncertainty in maritime accidents modelling using Bayesian network with interval probabilities. Saf Sci 102:211–225. https://doi.org/10.1016/j.ssci.2017.10.016
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yazdi, M. A review paper to examine the validity of Bayesian network to build rational consensus in subjective probabilistic failure analysis. Int J Syst Assur Eng Manag 10, 1–18 (2019). https://doi.org/10.1007/s13198-018-00757-7
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-018-00757-7