Simulator-based Human Reliability Analysis using Bayesian Network: A Case Study on Situation Awareness in Engine Resources Management

ABSTRACT: Situational awareness (SA) is regarded as one of the important non-technical skills in constructing the seafarers’ ability in daily decision-making and performing tasks, especially in Engine Resources Management (ERM). In maritime accidents that are mainly human error, insufficient SA is the specific factor that contributes to most of incidents. To quantitatively assess SA reliability, a Bayesian network of seafarers’ performance in attaining SA in engine supervisory control is constructed. The adaptation of simulator data helped as combination along using subject matter expert input, which is a common practice in constructing human reliability analysis. Additionally, the simulator data can serve as the updating function when new data is observed. The result shows that the model can provide promising results as compared with expert expectation. Such kind of model can support the evaluation of the engine operation onboard, and mitigation can be provided to reduce the probability of human error.

KEYWORDS: Situation Awareness (SA), Bayesian Networks, Human Reliability, Human Reliability Assessment (HRA), Maritime Operation, Engine Room Resource Management, Human Error Probability (HEP), Cognitive Reliability and Error Analysis (CREAM)

REFERENCES

Wróbel K. Searching for the origins of the myth: 80% human error impact on maritime safety. Reliability Engineering & System Safety 2021;216:107942. - doi:10.1016/j.ress.2021.107942

Grech MR, Horberry T, Smith A. Human Error in Maritime Operations: Analyses of Accident Reports Using the Leximancer Tool. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2002;46:1718–21. - doi:10.1177/154193120204601906

Cordon JR, Mestre JM, Walliser J. Human factors in seafaring: The role of situation awareness. Safety Science 2017;93:256–65. - doi:10.1016/j.ssci.2016.12.018

International Maritime Organization. International convention on standards of training, certification and watchkeeping for seafarers. London: International Maritime Organization; 2011.

Patriarca R. Human reliability analysis: Exploring the intellectual structure of a research field. Reliability Engineering and System Safety 2020;203:27. - doi:10.1016/j.ress.2020.107102

International Maritime Organization. Revised Guidelines For Formal Safety Assessment (FSA) For Use in The IMO Rule-making Process. 2018.

Yang ZL, Bonsall S, Wall A, Wang J, Usman M. A modified CREAM to human reliability quantification in marine engineering. Ocean Engineering 2013;58:293–303. - doi:10.1016/j.oceaneng.2012.11.003

Liu J, Aydin M, Akyuz E, Arslan O, Uflaz E, Kurt RE, et al. Prediction of human–machine interface (HMI) operational errors for maritime autonomous surface ships (MASS). J Mar Sci Technol 2021. - doi:10.1007/s00773-021-00834-w

Ahn SI, Kurt RE, Turan O. The hybrid method combined STPA and SLIM to assess the reliability of the human interaction system to the emergency shutdown system of LNG ship-to-ship bunkering. Ocean Engineering 2022;265:112643. - doi:10.1016/j.oceaneng.2022.112643

Mkrtchyan L, Podofillini L, Dang VN. Bayesian belief networks for human reliability analysis: A review of applications and gaps. Reliability Engineering & System Safety 2015;139:1–16. - doi:10.1016/j.ress.2015.02.006

Park J, Jung W, Kim J. Inter-relationships between performance shaping factors for human reliability analysis of nuclear power plants. Nuclear Engineering and Technology 2020;52:87–100. - doi:10.1016/j.net.2019.07.004

Røed W, Mosleh A, Vinnem JE, Aven T. On the use of the hybrid causal logic method in offshore risk analysis. Reliability Engineering & System Safety 2009;94:445–55. - doi:10.1016/j.ress.2008.04.003

Vinnem JE, Bye R, Gran BA, Kongsvik T, Nyheim OM, Okstad EH, et al. Risk modelling of maintenance work on major process equipment on offshore petroleum installations. Journal of Loss Prevention in the Process Industries 2012;25:274–92. - doi:10.1016/j.jlp.2011.11.001

Aydin M, Akyuz E, Turan O, Arslan O. Validation of risk analysis for ship collision in narrow waters by using fuzzy Bayesian networks approach. Ocean Engineering 2021;231:108973. - doi:10.1016/j.oceaneng.2021.108973

Musharraf M, Bradbury-Squires D, Khan F, Veitch B, MacKinnon S, Imtiaz S. A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis. Reliability Engineering & System Safety 2014;132:1–8. - doi:10.1016/j.ress.2014.06.016

Li P, Zhang L, Dai L, Li X-F. Study on operator’s SA reliability in digital NPPs. Part 3: A quantitative assessment method. Annals of Nuclear Energy 2017;109:82–91. - doi:10.1016/j.anucene.2017.05.019

Nizar AM, Miwa T, Uchida M. Measurement of situation awareness in engine control room: approach for non-technical skill assessment in engine resource management. WMU J Marit Affairs 2022;21:401–19. - doi:10.1007/s13437-022-00270-6

Hogg DN, Folles⊘ K, Strand-Volden F, Torralba B. Development of a situation awareness measure to evaluate advanced alarm systems in nuclear power plant control rooms. Ergonomics 1995;38:2394–413. - doi:10.1080/00140139508925275

Groth KM, Smith CL, Swiler LP. A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods. Reliability Engineering & System Safety 2014;128:32–40. - doi:10.1016/j.ress.2014.03.010

Citation note:

Nizar A.M., Miwa T., Uchida M.: Simulator-based Human Reliability Analysis using Bayesian Network: A Case Study on Situation Awareness in Engine Resources Management. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 18, No. 3, doi:10.12716/1001.18.03.13, pp. 593-599, 2024

BibTeX EndNote

Authors in other databases:

Adi Mas Nizar: ORCID iD icon

orcid.org/0000-0001-7748-3053 Scopus icon

57209604111

Takashi Miwa: Scopus icon

54946738100

Makoto Uchida: Scopus icon

7402278764

File downloaded 78 times