A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY

Melih Yucesan; Muhammet Gul; Ali Fuat Gunerı

doi:10.18186/thermal.871949

Research Article

Year 2021, Volume: 7 Issue: 2 - Special Issue 13: 2nd International Conference (ICRESE -2020), India,, 222 - 229, 01.02.2021

Melih Yucesan Muhammet Gul Ali Fuat Gunerı

https://doi.org/10.18186/thermal.871949

Cited By: 5

Abstract

References

[1] Khakzad N, Khan F, Amyotte, P. Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches. Reliab. Eng. Syst. Saf, 2011; 96:8: 925-32. https://doi.org/10.1016/j.ress.2011.03.012.
[2] Turhan C, Kazanasmaz T, Akkurt, GG. Performance indices of soft computing models to predict the heat load of buildings in terms of architectural indicators. J. Therm. Eng 2017; 3:4:1358-74. https://doi.org/10.18186/journal-of-thermal-engineering.330179.
[3] Zarei E, Khakzad N, Cozzani V, Reniers G. Safety analysis of process systems using Fuzzy Bayesian Network (FBN). J Loss Prev Process Ind 2019:57:7-16. https://doi.org/10.1016/j.jlp.2018.10.011.
[4] Hosseini S, Sarder MD. Development of a Bayesian network model for optimal site selection of electric vehicle charging station. Int J Elec Power 2019:105:110-22. https://doi.org/10.1016/j.ijepes.2018.08.011.
[5] Tong X, Fang W, Yuan S, Ma J, Bai Y. Application of Bayesian approach to the assessment of mine gas explosion. J Loss Prev Process Ind 2018:54:238-45. https://doi.org/10.1016/j.jlp.2018.04.003.
[6] Liu Z, Liu Y. A Bayesian network based method for reliability analysis of subsea blowout preventer control system. J Loss Prev Process Ind 2019:59:44-53. https://doi.org/10.1016/j.jlp.2019.03.004.
[7] Yazdi M, Kabir S. A fuzzy Bayesian network approach for risk analysis in process industries. Process Saf Environ 2017:111:507-19. https://doi.org/10.1016/j.psep.2017.08.015.
[8] Hamza Z, Hacene S. Reliability and safety analysis using fault tree and Bayesian networks. Integr Comput-Aid E 2019:11:1:73-86. https://doi.org/10.1504/IJCAET.2019.096720.
[9] Cai B, Liu Y, Liu Z, Chang Y, Jiang L. Risk analysis of subsea blowout preventer by mapping GO models into Bayesian networks. In Bayesian Networks for Reliability Engineering Singapore: Springer; 2020.
[10] Smith D, Veitch B, Khan F, Taylor R. Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches. J Loss Prev Process Ind 2017:45:88-101. https://doi.org/10.1016/j.jlp.2016.11.016.
[11] Abimbola M, Khan F, Khakzad N, Butt S. Safety and risk analysis of managed pressure drilling operation using Bayesian network. Saf. Sci 2015:76:133-44. https://doi.org/10.1016/j.ssci.2015.01.010.
[12] Yuan Z, Khakzad N, Khan F, Amyotte P. Risk analysis of dust explosion scenarios using Bayesian networks. Risk Anal. 2015:35:2:278-91. https://doi.org/10.1111/risa.12283.
[13] Hanea D, Ale B. Risk of human fatality in building fires: A decision tool using Bayesian networks. Fire Saf. J. 2009;44:5:704-10.
[14] Kang J, Sun L, Soares, CG. Fault Tree Analysis of floating offshore wind turbines. Renew. Energy 2019;133:1455-67. https://doi.org/10.1016/j.renene.2018.08.097.
[15] Badida P, Balasubramaniam, Y, Jayaprakash J. Risk evaluation of oil and natural gas pipelines due to natural hazards using fuzzy fault tree analysis. J Nat Gas Sci Eng 2019; 66:284-92.

A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY

Year 2021, Volume: 7 Issue: 2 - Special Issue 13: 2nd International Conference (ICRESE -2020), India,, 222 - 229, 01.02.2021

Melih Yucesan Muhammet Gul Ali Fuat Gunerı

https://doi.org/10.18186/thermal.871949

Cited By: 5

Abstract

Gun and rifle manufacturing contain various failures in the process of CNC machining, material supply, research & development, infrastructure and, operator. Due to these failures, the enterprise is exposed to great economic losses and a decrease in competition in the global market. In addition, failures in production cause events that seriously threaten human health. Failure analysis can increase safety by determining the cause of potential errors and taking measures for identified errors in the life cycle of the products. Therefore, this study employs a Bayesian Network (BN)-based modeling approach for capturing dependency among the basic events and obtaining top event probability. Firstly, a fault tree analysis (FTA) diagram is constructed, since its target is to pinpoint how basic event failures result in a top event (system) failure by an AND/OR logical gate. While, AND logical gate should take place in both cases, it is sufficient to realize one of the states in the OR logical gate. Then, a BN-based on fault tree transformation is applied. A case study in a leading weapon factory that produces various types of guns and rifles in the Black Sea region of Turkey is performed. For the application viewpoint, appropriate control measures can be taken into account to decrease the number of failed products based on the performed failure analysis.

Keywords

Bayesian Network, Fault Tree Analysis, Weapon Industry

References

[1] Khakzad N, Khan F, Amyotte, P. Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches. Reliab. Eng. Syst. Saf, 2011; 96:8: 925-32. https://doi.org/10.1016/j.ress.2011.03.012.
[2] Turhan C, Kazanasmaz T, Akkurt, GG. Performance indices of soft computing models to predict the heat load of buildings in terms of architectural indicators. J. Therm. Eng 2017; 3:4:1358-74. https://doi.org/10.18186/journal-of-thermal-engineering.330179.
[3] Zarei E, Khakzad N, Cozzani V, Reniers G. Safety analysis of process systems using Fuzzy Bayesian Network (FBN). J Loss Prev Process Ind 2019:57:7-16. https://doi.org/10.1016/j.jlp.2018.10.011.
[4] Hosseini S, Sarder MD. Development of a Bayesian network model for optimal site selection of electric vehicle charging station. Int J Elec Power 2019:105:110-22. https://doi.org/10.1016/j.ijepes.2018.08.011.
[5] Tong X, Fang W, Yuan S, Ma J, Bai Y. Application of Bayesian approach to the assessment of mine gas explosion. J Loss Prev Process Ind 2018:54:238-45. https://doi.org/10.1016/j.jlp.2018.04.003.
[6] Liu Z, Liu Y. A Bayesian network based method for reliability analysis of subsea blowout preventer control system. J Loss Prev Process Ind 2019:59:44-53. https://doi.org/10.1016/j.jlp.2019.03.004.
[7] Yazdi M, Kabir S. A fuzzy Bayesian network approach for risk analysis in process industries. Process Saf Environ 2017:111:507-19. https://doi.org/10.1016/j.psep.2017.08.015.
[8] Hamza Z, Hacene S. Reliability and safety analysis using fault tree and Bayesian networks. Integr Comput-Aid E 2019:11:1:73-86. https://doi.org/10.1504/IJCAET.2019.096720.
[9] Cai B, Liu Y, Liu Z, Chang Y, Jiang L. Risk analysis of subsea blowout preventer by mapping GO models into Bayesian networks. In Bayesian Networks for Reliability Engineering Singapore: Springer; 2020.
[10] Smith D, Veitch B, Khan F, Taylor R. Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches. J Loss Prev Process Ind 2017:45:88-101. https://doi.org/10.1016/j.jlp.2016.11.016.
[11] Abimbola M, Khan F, Khakzad N, Butt S. Safety and risk analysis of managed pressure drilling operation using Bayesian network. Saf. Sci 2015:76:133-44. https://doi.org/10.1016/j.ssci.2015.01.010.
[12] Yuan Z, Khakzad N, Khan F, Amyotte P. Risk analysis of dust explosion scenarios using Bayesian networks. Risk Anal. 2015:35:2:278-91. https://doi.org/10.1111/risa.12283.
[13] Hanea D, Ale B. Risk of human fatality in building fires: A decision tool using Bayesian networks. Fire Saf. J. 2009;44:5:704-10.
[14] Kang J, Sun L, Soares, CG. Fault Tree Analysis of floating offshore wind turbines. Renew. Energy 2019;133:1455-67. https://doi.org/10.1016/j.renene.2018.08.097.
[15] Badida P, Balasubramaniam, Y, Jayaprakash J. Risk evaluation of oil and natural gas pipelines due to natural hazards using fuzzy fault tree analysis. J Nat Gas Sci Eng 2019; 66:284-92.

There are 15 citations in total.

Details

Primary Language	English
Subjects	Engineering
Journal Section	Articles
Authors	Melih Yucesan This is me 0000-0001-6148-4959 Muhammet Gul 0000-0002-5319-4289 Ali Fuat Gunerı This is me 0000-0003-2525-7278
Publication Date	February 1, 2021
Submission Date	January 8, 2020
Published in Issue	Year 2021 Volume: 7 Issue: 2 - Special Issue 13: 2nd International Conference (ICRESE -2020), India,

Cite

APA	Yucesan, M., Gul, M., & Gunerı, A. F. (2021). A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY. Journal of Thermal Engineering, 7(2), 222-229. https://doi.org/10.18186/thermal.871949
AMA	Yucesan M, Gul M, Gunerı AF. A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY. Journal of Thermal Engineering. February 2021;7(2):222-229. doi:10.18186/thermal.871949
Chicago	Yucesan, Melih, Muhammet Gul, and Ali Fuat Gunerı. “A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY”. Journal of Thermal Engineering 7, no. 2 (February 2021): 222-29. https://doi.org/10.18186/thermal.871949.
EndNote	Yucesan M, Gul M, Gunerı AF (February 1, 2021) A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY. Journal of Thermal Engineering 7 2 222–229.
IEEE	M. Yucesan, M. Gul, and A. F. Gunerı, “A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY”, Journal of Thermal Engineering, vol. 7, no. 2, pp. 222–229, 2021, doi: 10.18186/thermal.871949.
ISNAD	Yucesan, Melih et al. “A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY”. Journal of Thermal Engineering 7/2 (February 2021), 222-229. https://doi.org/10.18186/thermal.871949.
JAMA	Yucesan M, Gul M, Gunerı AF. A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY. Journal of Thermal Engineering. 2021;7:222–229.
MLA	Yucesan, Melih et al. “A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY”. Journal of Thermal Engineering, vol. 7, no. 2, 2021, pp. 222-9, doi:10.18186/thermal.871949.
Vancouver	Yucesan M, Gul M, Gunerı AF. A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY. Journal of Thermal Engineering. 2021;7(2):222-9.