Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-25T06:47:53.842Z Has data issue: false hasContentIssue false
  • English
  • Français

A civil aviation safety assessment model using a bayesian belief network (BBN)

Published online by Cambridge University Press:  03 February 2016

R. Greenberg ,
S. C. Cook  and
D. Harris
R. Greenberg
Affiliation:
Systems Engineering and Evaluation Centre, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
S. C. Cook
Affiliation:
Systems Engineering and Evaluation Centre, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
D. Harris
Affiliation:
Systems Engineering and Evaluation Centre, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper we present a Bayesian belief network (BBN) sociotechnical model for investigating the accident rate for multi-crew civil airline aircraft. The model emphasises the influence of airline policy and societal behaviour patterns on the pilots within the piloting system. The main claim of this paper is that a BBN can be used to bring most aviation safety-critical elements into a common quantitative safety assessment despite the unique problems posed by the very low probability of accidents. We support this claim by replicating certain phenomena such as the low accident rate, the difference between the ‘more’ and ‘less’ safe airlines and other statistical factors of civil aviation. In particular, the model succeeds in explaining the large gap of six to seven orders of magnitude between in-flight measurements of pilots’ error and accident rate.

Type
Research Article
Information
The Aeronautical Journal , Volume 109 , Issue 1102 , December 2005 , pp. I - XII
Copyright
Copyright © Royal Aeronautical Society 2005 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. AirDisaster, viewed 15 August, 2004, http://www.airdisaster.com/statistics/yearly.shtml. Google Scholar
2. Gore, A, Vice President (Chairman) Final Report to President Clinton. White House Commission on Aviation Safety and Security, 12 February 1987. Viewed 28 May 2004. http://avsp.larc.nasa.gov/WHC_Aviation_Safety.pdf, 1987.Google Scholar
3. Bouisson, M., Martin, F. and Ourghanlian, A.. Assessment of a safety critical system including software: A Bayesian belief network for evidence sources, 1999 Proceedings of the Annual Reliability and Maintainability Symposium, 1999, pp 142–150.Google Scholar
4. Boeing, Statistical Summary of Commercial Jet Airplane Accidents, World Operations 1959 – 2002, 2003.Google Scholar
4. Shi-Lie, C. (GARY) and Li, L.. Modelling team member characteristics for the formation of a multifunctional team in concurrent engineering, IEEE Transactions on Engineering Management, 2004, 51, (2), pp 111–124.Google Scholar
6. Cook, S.C., Harris, D.D. and Al-Zubaidy, S.. A systems approach to modelling the piloting system in multi-crew aircraft. In proceedings of the 10th Australian International Aerospace Congress and 14th National Space Engineering Symposium, 29/7/03-1/8/03, 2003, Brisbane, Australia.Google Scholar
7. Degani, A. and Weiner, E.L.. Procedures in complex systems: The airline cockpit, IEEE Transactions on Systems, Man Cybernetics – Part A, Systems Humans, 27, (3), pp #02–312, 1997.Google Scholar
8. Faith, N., Black Box: The Further Investigations, Boxtree, Great Britain 1998.Google Scholar
9. Fidel, R.. Collaborative Information Retrieval (CIR), viewed January 15, 2004, http://www.ischool.washington.edu/cir/ISICtextWeb.doc, 2003.Google Scholar
10. Filar, J.A.. Mathematical models, In: knowledge for sustainable development: An Insight into the Encyclopaedia of Life Support Systems, UNESCO/EOLSS, released at the World Summit on Sustainable Development, September 2002, Johannesburg, South Africa.Google Scholar
11. Ginnett, R.C., Crews as groups: their formation and their leadership. In Wiener, E.L., Kanki, B.G. and Helmreich, R.L.. Cockpit Resource Management, 1995, Academic Press, pp 71–98.Google Scholar
12. Graeber, R.C. and Moodi, M.M.. Understanding flight crew adherence to procedures: The procedural event analysis tool (PEAT). Report of Boeing Commercial Airplane Group Seattle, Washington, USA Safety seminar Capetown, South Africa November 17–19, 1998.Google Scholar
13. Greenberg, R., Cook, S.C. and Harris, D.D.. A probability model of the piloting system in large passenger transport aircraft. In proceedings of the 10th Australian International Aerospace Congress and 14th National Space Engineering Symposium, 29/7/03-1/8/03, 2003, Brisbane, Australia.Google Scholar
14. Harris, D.D.. Quantifying accident precipitating events, To be published, 2005.Google Scholar
15. Helmreich, R.L., Klinect, J.R., and Wilhelm, J.A.. Models of threat, error, and CRM in flight operations. Proceedings of the 10th International Symposium on Aviation Psychology Columbus, Ohio 3–6 May, 1999.Google Scholar
16. Helmreich, R.L., Klinect, J.R. and Wilhelm, J.A, Threat and error management: Data from line operations safety audits. Proceedings of the 10th International Symposium on Aviation Psychology Columbus, Ohio 3–6 May 1999.Google Scholar
17. Hopkins, H., Managing Major Hazards: The Lessons of the Moura Mine Disaster, Allen & Unwin, 1999.Google Scholar
18. Howard, R.W.. Planning for super safety: The fail-safe dimension, Aeronaut J, 2000, 104, (1041), pp 517–555.Google Scholar
19. Katzenbach, J.R. and Smith, D.K., The Wisdom of teams, Boston, MA. 1993. The actual discussion is brought in Snook, S.A., Friendly Fire, Prinston University Pres, 2000.Google Scholar
20. Leveson, N.G., Safeware: system safety and computers, Addison-Wesley, 1995.Google Scholar
21. Leveson, N.G.. An accident model for engineering safer systems, Safety Science, 2000, 42, pp 237–270.Google Scholar
22. Manion, M., and Evan, W.M.. Technological catastrophes: their causes and prevention, Technology in Society, 2002, 24, pp 207–224.Google Scholar
23. Moore, W.H.. The grounding of Exxon Waldez: An examine of the human and organizational factors, Marine Technology, 1994, 31, (1), pp 41–51.Google Scholar
24. Neapolitan, R.E., Probabilistic Reasoning in Expert Systems: Theory and Algorithms, John Wiley & Sons, Inc 1990.Google Scholar
25. Neil, M., Fenton, N., Forey, S. and Harris, R.. Using Bayesian belief networks to predict the reliability of military vehicles, Computing & Control, Engineering J, 11–20 February 2001.Google Scholar
26. Netica, , Details on NETICA can be found in www.norsys.com.Google Scholar
27. NTSB Aircraft Accident Report, Runway Overrun During Landing, American Airlines Flight 1420, McDonnell Douglas MD-82, N215AA, Little Rock, Arkansas, 1 June, 1999. Viewed, 3 September, 2004, http://www.ntsb.gov/Publictn/2001/AAR0102.pdf. 1999.Google Scholar
28. Park, J., Kim, J. and Jung, W.. Comparing the complexity of procedural steps with the operators performance observed under stressful conditions, Reliability Engineering and System Safety, 83, pp 79–91, 2004.Google Scholar
29. Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, 1991.Google Scholar
30. PlaneCrashInfo, This figure along with other important statistical information can be found in http://www.planecrashinfo.com/cause.htm.Google Scholar
31. Proctor, P.. Boeing safety tool provides insights into human factors errors, Aviation Week Space and Technology, 21 June 1998.Google Scholar
32. Reason, J., Human Error, Cambridge University Press, 1990.Google Scholar
33. Reason, J., Managing the Risks of Organizational Accidents, Ashgate Google Scholar
34. Roth, E.M., Mumaw, R.J. and Lewis, P.M.. An empirical investigation of operator performance in cognitively demanding simulated emergencies, NUREG/CR-6208, Washington DC, US Nuclear Regulatory Commission (1992).Google Scholar
35. Shappell, S.A. and Wiegmann, D.A.. The Human factors analysis and classification system-HFACS, Report DOT/FAA/AM-00/7 of the Office of Aviation Medicine, FAA, 2000.Google Scholar
36. Wiegmann, D.A. and Shappell, S.A., A human error analysis of commercial aviation accidents using the human factors analysis and classification system (HFACS), Report DOT/FAA/AM-01/3 of the Office of Aviation Medicine, FAA, 2001.Google Scholar
37. Svenson, O.. The accident evolution and barrier function (AEB) Model Applied to Incident Analysis in the Processing Industries, Risk Analysis, 1991, 11, (3), 499–507.Google Scholar
38. Thomas, M.J.W.. Improving organisational safety through the integrated evaluation of operational and training performance: An adaptation of the Line Operations Safety Audit (LOSA) methodology. Human Factors and Aerospace Safety, 2003, 3, (1), pp 25–45.Google Scholar
39. Tolstykh, V.. Brief Summary of incident reporting system events with procedural deficiencies, Proceedings of a Specialists Meeting on Operating Procedures of Nuclear Power Plants and Their Presentations, International Atomic Energy Agency, 1992.Google Scholar
40. Wiley, J.. Right talk from the right seat, Business and Commercial Aviation, July 2001, pp 48–54, 2001.Google Scholar