Analysis of General Aviation fixed-wing aircraft accidents involving inflight loss of control using a state-based approach
Abstract
Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source of accident data, but conventional analyses of the database yield limited insights to LOC-I. We investigate the causes of 5,726 LOC-I fixed‑wing GA aircraft accidents in the United States in 1999–2008 and 2009–2017 using a state-based modeling approach. The multi-year analysis helps discern changes in causation trends over the last two decades. Our analysis highlights LOC-I causes such as pilot actions and mechanical issues that were not discernible in previous research efforts. The logic rules in the state-based approach help infer missing information from the National Transportation Safety Board (NTSB) accident reports. We inferred that 4.84% (1999–2008) and 7.46% (2009–2017) of LOC-I accidents involved a preflight hazardous aircraft condition. We also inferred that 20.11% (1999–2008) and 19.59% (2009–2017) of LOC-I accidents happened because the aircraft hit an object or terrain. By removing redundant coding and identifying when codes are missing, the state-based approach potentially provides a more consistent way of coding accidents compared to the current coding system.
Keyword : General Aviation safety, General Aviation accidents, loss of control, accident modeling, NTSB database
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aircraft Owners and Pilots Association. (2018). 27th Joseph T. Nall report: General aviation accidents in 2015. AOPA. https://www.aopa.org/-/media/files/aopa/home/training-and-safety/nall-report/27thnallreport2018.pdf?la=en&hash=C52F88B38FD95CB7C0A43F3B587A12E2692A8502
Ancel, E., & Shih, A. (2012, September). The analysis of the contribution of human factors to the in-flight loss of control accidents. In 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference (p. 5548). Indianapolis, Indiana. https://doi.org/10.2514/6.2012-5548
Ancel, E., Shih, A. T., Jones, S. M., Reveley, M. S., Luxhøj, J. T., & Evans, J. K. (2015). Predictive safety analytics: Inferring aviation accident shaping factors and causation. Journal of Risk Research, 18(4), 428–451. https://doi.org/10.1080/13669877.2014.896402
Ayra, E. S., Ríos Insua, D., & Cano, J. (2019). Bayesian network for managing runway overruns in aviation safety. Journal of Aerospace Information Systems, 16(12), 546–558. https://doi.org/10.2514/1.I010726
Ballard, S.-B., Beaty, L. P., & Baker, S. P. (2013). US Commercial air tour crashes 2000–2011: Burden, fatal risk factors and FIA Score Validation. Accident Analysis & Prevention, 57, 49–54. https://doi.org/10.1016/j.aap.2013.03.028
Bazargan, M., & Guzhva, V. S. (2007). Factors contributing to fatalities in general aviation. World Review of Intermodal Transportation Research, 1(2), 170–182. https://doi.org/10.1504/WRITR.2007.013949
Boyd, D. (2015). Causes and risk factors for fatal accidents in non-commercial twin engine piston general aviation aircraft. Accident Analysis and Prevention, 77, 113–119. https://doi.org/10.1016/j.aap.2015.01.021
Boyd, D. D., & Stolzer, A. (2016). Accident-precipitating factors for crashes in turbine-powered general aviation aircraft. Accident Analysis & Prevention, 86, 209–216. https://doi.org/10.1016/j.aap.2015.10.024
Federal Aviation Administration. (2019). Fly Safe: Prevent loss of control accidents. FAA. https://www.faa.gov/newsroom/flysafe-prevent-loss-control-accidents-34?newsId=94566
Franza, A., & Fanjoy, R. (2012). Contributing factors in Piper PA28 and cirrus SR20 aircraft accidents. Journal of Aviation Technology and Engineering, 1(22), 90–96. https://doi.org/10.5703/1288284314662
Fultz, A. J., & Ashley, W. S. (2016). Fatal weather-related general aviation accidents in the United States. Physical Geography, 37(5), 291–312. https://doi.org/10.1080/02723646.2016.1211854
General Aviation Joint Steering Committee. (2016). GAJSC Pareto. GAJSC. https://www.gajsc.org/2016/01/ga-safety-performance-fy16/pareto/
Goldman, S. M., Fiedler, E. R., & King, R. E. (2002). General aviation maintenance-related accidents: A review of ten years (1988–1997) of NTSB Data. DOT/FAA/AM-02/23. Office of Aerospace Medicine, Washington. https://www.faa.gov/data_research/research/med_humanfacs/oamtechreports/2000s/media/0223.pdf
Houston, S. J., Walton, R. O., & Conway, B. A. (2012). Analysis of General Aviation instructional loss of control accidents. The Journal of Aviation/Aerospace Education and Research, 22(1), 35–49. https://doi.org/10.15394/jaaer.2012.1402
National Transportation Safety Board. (1998). Aviation Coding Manual. Washington DC, National Transportation Safety Board. NTSB. https://www.ntsb.gov/GILS/Documents/codman.pdf
National Transportation Safety Board. (2019a). Aviation accident database & synopses. NTSB. https://www.ntsb.gov/_layouts/ntsb.aviation/index.aspx
National Transportation Safety Board. (2019b). Government Information Locator Service (GILS): Aviation accident database. NTSB. https://www.ntsb.gov/GILS/Pages/AviationAccident.aspx
Rao, A. H. (2016). A new approach to modeling aviation accidents [Doctoral dissertation, Purdue University, USA]. https://docs.lib.purdue.edu/open_access_dissertations/993
Rao, A. H., & Marais, K. (2015). Identifying high-risk occurrence chains in helicopter operations from accident data. In 15th AIAA Aviation Technology, Integration, and Operations Conference (p. 2848). American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2015-2848
Rao, A. H., Fala, N., & Marais, K. (2016). Analysis of helicopter maintenance risk from accident data. In AIAA Infotech @ Aerospace (p. 2135). American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2016-2135
Rao, A. H., & Marais, K. (2020). A state-based approach to modeling general aviation accidents. Reliability Engineering and System Safety, 193, 106670. https://doi.org/10.1016/j.ress.2019.106670
Sorenson, D. & Marais, K. (2016). Patterns of causation in accidents and other systems engineering failures. In IEEE Systems Conference. IEEE, Orlando, FL. https://doi.org/10.1109/SYSCON.2016.7490568
Ud-Din, S., & Yoon, Y. (2018). Analysis of loss of control parameters for aircraft maneuvering in general aviation. Journal of Advanced Transportation, 2018, 7865362. https://doi.org/10.1155/2018/7865362
Uğurlu, Ö., Yıldız, S., Loughney, S., Wang, J., Kuntchulia, S., & Sharabidze, I. (2020). Analyzing collision, grounding, and sinking accidents occurring in the Black Sea utilizing HFACS and Bayesian networks. Risk Analysis, 40(12), 2610–2638. https://doi.org/10.1111/risa.13568
Wiegmann, D., Faaborg, T., Boquet, A., Detwiler, C., Holcomb, K., & Shappell, S. (2005). Human error and general aviation accidents: A comprehensive, fine-grained analysis using HFACS. Office of Aerospace Medicine, Washington, DC. https://apps.dtic.mil/dtic/tr/fulltext/u2/a460866.pdf
Xiao, Q., Luo, F., & Li, Y. (2020). Risk assessment of seaplane operation safety using Bayesian network. Symmetry, 12(6), 888. https://doi.org/10.3390/sym12060888