Fault Risk Assessment of Underwater Vehicle Steering System Based on Virtual Prototyping and Monte Carlo Simulation

Open access


Assessing the risks of steering system faults in underwater vehicles is a human-machine-environment (HME) systematic safety field that studies faults in the steering system itself, the driver’s human reliability (HR) and various environmental conditions. This paper proposed a fault risk assessment method for an underwater vehicle steering system based on virtual prototyping and Monte Carlo simulation. A virtual steering system prototype was established and validated to rectify a lack of historic fault data. Fault injection and simulation were conducted to acquire fault simulation data. A Monte Carlo simulation was adopted that integrated randomness due to the human operator and environment. Randomness and uncertainty of the human, machine and environment were integrated in the method to obtain a probabilistic risk indicator. To verify the proposed method, a case of stuck rudder fault (SRF) risk assessment was studied. This method may provide a novel solution for fault risk assessment of a vehicle or other general HME system.

1. Zhao Tingdi, Safety Design, Analysis and Validation. 2011, Beijing: National Defense Industry Press.

2. Qin Liyan, Shao Chunfu, and Jia Hongfei, Analysis on Express Way Traffic Accidents and Their Countermeasures. China Safety Science Journal, 2003. 13(6): p. 64-67.

3. LI Jin-long and S. Wan-hu, Cause Analysis ofTraffic Accidents on Express Highway and Study on Their Countermeasures. China Safety Science Journal, 2005. 15(1): p. 59-62.

4. Mei-Chen Hsueh, T.K.T., and Ravishankar K. Iyer, Fault Injection Techniques and Tools. Computer, 1997(April): p. 75-82.

5. Carmel, Y., et al., Assessing fire risk using Monte Carlo simulations of fire spread. Forest Ecology and Management, 2009. 257(1): p. 370-377.

6. Sarajcev, P., J. Vasilj, and R. Goic, Monte Carlo analysis of wind farm surge arresters risk of failure due to lightning surges. Renewable Energy, 2013. 57(0): p. 626-634.

7. Rocha, J.M., A.A. Henriques, and R. Calcada, Probabilistic safety assessment of a short span high-speed railway bridge. Engineering Structures, 2014. 71(0): p. 99-111.

8. Olaru, M., M. Şandru, and I.C. Pirnea, Monte Carlo Method Application for Environmental Risks Impact Assessment in Investment Projects. Procedia - Social and Behavioral Sciences, 2014. 109(0): p. 940-943.

9. Lonati, G. and F. Zanoni, Monte-Carlo human health risk assessment of mercury emissions from a MSW gasification plant. Waste Management, 2013. 33(2): p. 347-355.

10. LeBlanc, D.I., et al., A national produce supply chain database for food safety risk analysis. Journal of Food Engineering, 2015. 147(0): p. 24-38.

11. Amigun, B., D. Petrie, and J. Gorgens, Economic risk assessment of advanced process technologies for bioethanol production in South Africa: Monte Carlo analysis. Renewable Energy, 2011. 36(11): p. 3178-3186.

12. K. Durga Rao, V. Gopika, V.V.S. Sanyasi Rao, H.S. Kushwaha, A.K. Verma, A. Srividy, Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment. Reliability Engineering and System Safety, 2009(94): p. 872-883.

13. Montewka, J., et al., A framework for risk assessment for maritime transportation systems -A case study for open sea collisions involving RoPax vessels. Reliability Engineering and System Safety, 2014(124): p. 142-157.

14. Ferrario, E. and E. Zio, Goal Tree Success Tree-Dynamic Master Logic Diagram and Monte Carlo simulation for the safety and resilience assessment of a multistate system of systems. Engineering Structures, 2014. 59(0): p. 411-433.

15. Smid, J.H., et al., Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment. International Journal of Food Microbiology, 2010. 139, Supplement(0): p. S57-S63.

16. Faghih-Roohi, S., M. Xie, and K.M. Ng, Accident risk assessment in marine transportation via Markov modelling and Markov Chain Monte Carlo simulation. Ocean Engineering, 2014. 91(0): p. 363-370.

17. Chen, H., L. Li, and Y. Sun, Risk Assessment of Aero Engine Failure based on Monte Carlo Simulation. Procedia Engineering, 2014. 80(0): p. 415-423.

18. Sobhani, A., W. Young, and M. Sarvi, A simulation based approach to assess the safety performance of road locations. Transportation Research Part C: Emerging Technologies, 2013. 32(0): p. 144-158.

19. Chen, F. and S. Chen, Probabilistic Assessment of Vehicle Safety under Various Driving Conditions: A Reliability Approach. Procedia - Social and Behavioral Sciences, 2013. 96(0): p. 2414-2424.

20. HU Liang-mou, CAO Ke-qiang, and XU Hao-jun, Fault Diagnosis for Hydraulic Actuator Double Closed-loop System Based on Improved LS-SVM. Journal of System Simulation, 2009. 21(17): p. 5477-5480.

21. Zhe Cheng. A Hybrid Prognostics Approach to Estimate the Residual Useful Life of a Planetary Gearbox with a Local Defect, Journal of Vibroengineering, 2015, 17(2): 682-694.

22. Yang Chen, G.C., Zhenpeng Zhang, Yulong Huang, Multi-field coupling dynamic modeling and simulation of turbine test rig gas system. Simulation Modelling Practice and Theory, 2014(44): p. 95-118.

23. Balci, O., Verification, Validation and Accreditation, in Winter Simulation Conference. 1998. p. 41-48.

24. 5000.61, DoD, Verification Validation and Accreditation (VV&A) Recommended Practice Guide, 1996.

25. MIL-STD-3022, DoD, Documentation of Verification, Validation, and Accreditation (VV&A) for Models and Simulations 2008.

26. WANG Jing-qi, SHI Sheng-da, and ZHANG Wei-kang, Safe Maneuvering Technology for Ensuring Submarine’s Survivability. Journal of Naval University of Engineering, 2009. 21(1): p. 63-67.

27. Khan, F.I., P.R. Amyotte, and D.G. DiMattia, HEPI: A new tool for human error probability calculation for offshore operation. Safety Science, 2006. 44(4): p. 313-334.

28. Park, K.S. and J.i. Lee, A new method for estimating human error probabilities: AHP-SLIM. Reliability Engineering & System Safety, 2008. 93(4): p. 578-587.

Polish Maritime Research

The Journal of Gdansk University of Technology

Journal Information

IMPACT FACTOR 2017: 0.763
5-year IMPACT FACTOR: 0.816

CiteScore 2017: 0.99

SCImago Journal Rank (SJR) 2017: 0.280
Source Normalized Impact per Paper (SNIP) 2017: 0.788


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 124 124 16
PDF Downloads 44 44 8