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High risk occurrence chains in helicopter accidents

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  • Rao, Arjun H.
  • Marais, Karen

Abstract

We develop an approach to identify high-risk sequences of events (or occurrence chains) in General Aviation (GA) accidents using historical accident data. We demonstrate our approach on the 6180 civil helicopter accidents that occurred in the United States between 1982 and 2015. We begin by providing a detailed guide to the NTSB accident database including of the pre- and post-2008 coding systems. We compare the most frequently occurring chains for different mission types and injury severity levels. The single node inflight loss of control (LOC) occurrence chain appeared in 12.5% of helicopter accidents between 1982 and 2015, appearing most frequently in both fatal and non-fatal accidents. Students’ (and sometimes instructors’) inability to perform safe autorotations or improper autorotation recoveries most often resulted in hard landings. As a first step to reducing these unfortunate training accidents, we recommend conducting an in-depth study to identify high-risk training maneuvers and hazardous situations faced by student pilots. We provide recommendations to improve the NTSB accident database and recording system that could potentially help mitigate the top causes for GA accidents.

Suggested Citation

  • Rao, Arjun H. & Marais, Karen, 2018. "High risk occurrence chains in helicopter accidents," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 83-98.
    • Handle: RePEc:eee:reensy:v:170:y:2018:i:c:p:83-98
    DOI: 10.1016/j.ress.2017.10.014
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    References listed on IDEAS

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    1. Tsagkas, Vassilis & Nathanael, Dimitris & Marmaras, Nicolas, 2014. "A pragmatic mapping of factors behind deviating acts in aircraft maintenance," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 106-114.
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    Cited by:

    1. Rao, Arjun H. & Marais, Karen, 2020. "A state-based approach to modeling general aviation accidents," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. Chen, Fangyu & Wang, Hongwei & Xu, Gangyan & Ji, Hongchang & Ding, Shanlei & Wei, Yongchang, 2020. "Data-driven safety enhancing strategies for risk networks in construction engineering," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    3. Wang, Lei & Liu, Qing & Dong, Shiyu & Guedes Soares, C., 2022. "Selection of countermeasure portfolio for shipping safety with consideration of investment risk aversion," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Omrani, Farzane & Etemadfard, Hossein & Shad, Rouzbeh, 2024. "Assessment of aviation accident datasets in severity prediction through machine learning," Journal of Air Transport Management, Elsevier, vol. 115(C).
    5. Cankaya, Burak & Topuz, Kazim & Delen, Dursun & Glassman, Aaron, 2023. "Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents," Omega, Elsevier, vol. 120(C).

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