Risk Analysis of Equipment Loss During Marine Survey Operation by Integrating Fault Tree to Bayesian Network

Dwitya Harits Waskito, Ahmad Muhtadi, Dimas Fajar Prasetyo, Indra Kurniawan, Dwi Haryanto, Adi Slamet Riyadi

Abstract


The process of deploying and towing the survey equipment for several marine survey activities is essential since it visualises the seabed and improves data accuracy. Since the equipment is deployed to an underwater level, the risk arises with the deployment. These risks include potential contact with submerged objects and the seabed, which can result in the loss of equipment and have detrimental environmental consequences. This study aims to analyse the risk-associated factors related to the loss of survey equipment using Fault Tree Analysis (FTA) and Bayesian Network (BN). The constructed FTA was converted into BN to find the relationship between Basic events and simulate the probability of updating Basic events. The sensitivity analysis results of the BN model indicate that "Procedure Failure" is the Basic contributor to the loss of survey equipment. The findings from this study will have practical implications for stakeholders, enabling them to enhance the safety of marine survey activities, particularly by mitigating the occurrence of equipment loss during operational procedures.



Keywords


bayesian network, FTA, marine survey, research vessel

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References


Asuelimen, G.; Blanco-Davis, E.; Wang, J.; Yang, Z.; Matellini, D. B. Formal Safety Assessment of a Marine Seismic Survey Vessel Operation, Incorporating Risk Matrix and Fault Tree Analysis. Journal of Marine Science and Application 2020, 19 (2), 155–172. https://doi.org/10.1007/s11804-020-00136-4

Muhtadi, A.; Waskito, D. H.; Prasetyo, D. F. Improving Safety of Marine Cable Survey Operation Through Safety Assessment Using the Formal Safety Assessment Method ( Case Study RV Baruna Jaya). In IOP Conference Series: Earth and Environmental Science; IOP Publishing, 2023. https://doi.org/10.1088/1755-1315/1166/1/012031

Lampis, M.; Andrews, J. D. Bayesian Beliefnetworks for Systemfault Diagnostics. Qual Reliab Eng Int 2009, 25 (4), 409–426. https://doi.org/10.1002/qre.978

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), 925–932. https://doi.org/10.1016/j.ress.2011.03.012

Sharma, P.; Kulkarni, M. S. Bayesian Belief Network for Assessing Impact of Factors on Army's Lean-Agile Replenishment System. Journal of Military Studies 2016, 7 (1), 11–23. https://doi.org/10.1515/jms-2016-0002

Duan, R.; Zhou, H. A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks. Energy Procedia 2012, 17, 1376–1382. https://doi.org/10.1016/j.egypro.2012.02.255

Trucco, P.; Cagno, E.; Ruggeri, F.; Grande, O. A Bayesian Belief Network Modelling of Organisational Factors in Risk Analysis: A Case Study in Maritime Transportation. Reliab Eng Syst Saf 2008, 93 (6), 845–856. https://doi.org/10.1016/j.ress.2007.03.035

Chen, P.; Mou, J.; Li, Y. Risk Analysis of Maritime Accidents in an Estuary: A Case Study of Shenzhen Waters. 2015, 42 (114), 54–62

Bian, H.; Zhang, J.; Li, R.; Zhao, H.; Wang, X.; Bai, Y. Risk Analysis of Tripping Accidents of Power Grid Caused by Typical Natural Hazards Based on FTA-BN Model. Natural Hazards 2021, 106 (3), 1771–1795. https://doi.org/10.1007/s11069-021-04510-5

Bobbio, A. Portinale, L. Minichino, M. Ciancamerla, E. Improving the Analysis of Dependable Systems by Mapping Fault Trees into Bayesian Networks. Reliability Engineering & System Safety. Reliability Engineering and System Safety 71 2001, 71, 249–260

Ansori, I.; Waskito, D. H.; Mutharuddin, M.; Irawati, N.; Nugroho, S.; Subaryata, S.; Mardiana, T. S.; Siregar, N. A. M. Enhancing Brake System Evaluation in Periodic Testing of Goods Transport Vehicles through FTA-FMEA Risk Analysis. Automotive Experiences 2023, 6 (2), 320–335. https://doi.org/https://doi.org/10.31603/ae.8394

Atehnjia, D. N.; Zaili, Y.; Wang, J. Application of Fault Tree – Bayesian Network for Graving Dock Gate Failure Analysis. International Journal of Advance in Scientific Research and Engineering 2018, 4 (1), 27–37. https://doi.org/10.7324/ijasre.2018.32576

Sokukcu, M.; Sakar, C. Risk Analysis of Collision Accidents during Underway STS Berthing Maneuver through Integrating Fault Tree Analysis (FTA) into Bayesian Network (BN). Applied Ocean Research 2022, 126 (July), 103290. https://doi.org/10.1016/j.apor.2022.103290

Sakar, C.; Toz, A. C.; Buber, M.; Koseoglu, B. Risk Analysis of Grounding Accidents By Mapping a Fault Tree Into a Bayesian Network. Applied Ocean Research 2021, 113 (June), 102764. https://doi.org/10.1016/j.apor.2021.102764

Li, H.; Ren, X.; Yang, Z. Data-Driven Bayesian Network for Risk Analysis of Global Maritime Accidents. Reliab Eng Syst Saf 2023, 230 (October 2022), 108938. https://doi.org/10.1016/j.ress.2022.108938

Zhao, X.; Yuan, H. Autonomous Vessels in the Yangtze River : A Study on the Maritime Accidents Using Data-Driven Bayesian Networks. 2021

Zhang, G.; Thai, V. V. Expert Elicitation and Bayesian Network Modeling for Shipping Accidents: A Literature Review. Saf Sci 2016, 87, 53–62. https://doi.org/10.1016/j.ssci.2016.03.019

Wu, B.; Tang, Y.; Yan, X.; Guedes, C. Bayesian Network Modelling for Safety Management of Electric Vehicles Transported in RoPax Ships. Reliab Eng Syst Saf 2021, 209 (June 2020), 107466. https://doi.org/10.1016/j.ress.2021.107466

Li, Y.; Cheng, Z.; Yip, T. L.; Fan, X.; Wu, B. Use of HFACS and Bayesian Network for Human and Organizational Factors Analysis of Ship Collision Accidents in the Yangtze River. Maritime Policy and Management 2022, 49 (8), 1169–1183. https://doi.org/10.1080/03088839.2021.1946609

Ugurlu, O.; Yildiz, S.; Loughney, S.; Wang, J.; Kuntchulia, S.; Sharabidze, I. Analyzing Collision, Grounding, and Sinking Accidents Occurring in the Black Sea Utilising HFACS and Bayesian Networks. 2020

Cai, M.; Zhang, J.; Zhang, D.; Yuan, X.; Soares, C. G. Collision Risk Analysis on Ferry Ships in Jiangsu Section of the Yangtze River Based on AIS Data. Reliab Eng Syst Saf 2021, 215 (December 2020), 107901. https://doi.org/10.1016/j.ress.2021.107901

Jiang, M.; Lu, J. The Analysis of Maritime Piracy Occurred in Southeast Asia by Using Bayesian Network. Transportation Research Part E 2020, 139 (1), 101965. https://doi.org/10.1016/j.tre.2020.101965

Uğurlu, F.; Yıldız, S.; Boran, M.; Uğurlu, Ö.; Wang, J. Analysis of Fishing Vessel Accidents with Bayesian Network and Chi-Square Methods. Ocean Engineering 2020, 198 (August 2019). https://doi.org/10.1016/j.oceaneng.2020.106956

Likun, W.; Zaili, Y. Bayesian Network Modelling and Analysis of Accident Severity in Waterborne Transportation : A Case Study in China. 2018, 180 (February), 277–289. https://doi.org/10.1016/j.ress.2018.07.021

Waskito, D. H.; Bowo, L. P.; Kurnia, S. H. M.; Kurniawan, I.; Nugroho, S.; Irawati, N.; Mutharuddin; Mardiana, T. S.; Subaryata. Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models. Safety 2024, 10 (1), 8. https://doi.org/https://doi.org/10.3390/safety10010008

Fan, S.; Yang, Z.; Blanco-Davis, E.; Zhang, J.; Yan, X. Analysis of Maritime Transport Accidents Using Bayesian Networks. Proc Inst Mech Eng O J Risk Reliab 2020, 234 (3), 439–454. https://doi.org/10.1177/1748006X19900850

Jia, Y.; Zhuang, Y.; Wang, F.; Lyu, P. Causes Analysis of Ship Collision Accidents Using Bayesian Network. The 28th International Ocean and Polar Engineering Conference. June 10, 2018, p ISOPE-I-18-099

Huang, J. C.; Nieh, C. Y.; Kuo, H. C. Risk Assessment of Ships Maneuvering in an Approaching Channel Based on AIS Data. Ocean Engineering 2019, 173 (November 2018), 399–414. https://doi.org/10.1016/j.oceaneng.2018.12.058

John, A.; Yang, Z.; Riahi, R.; Wang, J. A Risk Assessment Approach to Improve the Resilience of a Seaport System Using Bayesian Networks. Ocean Engineering 2016, 111, 136–147. https://doi.org/10.1016/j.oceaneng.2015.10.048

Schietekat, S.; De Waal, A.; Gopaul, K. G. Validation & Verification of a Bayesian Network Model for Aircraft Vulnerability. http://hdl.handle.net/10204/9209

Pristrom, S.; Yang, Z.; Wang, J.; Yan, X. A Novel Flexible Model for Piracy and Robbery Assessment of Merchant Ship Operations. Reliab Eng Syst Saf 2016, 155, 196–211. https://doi.org/10.1016/j.ress.2016.07.001

Jones, B.; Jenkinson, I.; Yang, Z.; Wang, J. The Use of Bayesian Network Modelling for Maintenance Planning in a Manufacturing Industry. Reliab Eng Syst Saf 2010, 95 (3), 267–277. https://doi.org/10.1016/j.ress.2009.10.007

Cai, B.; Liu, Y.; Zhang, Y.; Fan, Q.; Liu, Z.; Tian, X. A Dynamic Bayesian Networks Modeling of Human Factors on Offshore Blowouts. J Loss Prev Process Ind 2013, 26 (4), 639–649. https://doi.org/10.1016/j.jlp.2013.01.001




DOI: http://dx.doi.org/10.12962/j25800914.v8i1.20466

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