Hostname: page-component-848d4c4894-8kt4b Total loading time: 0 Render date: 2024-06-14T20:53:23.277Z Has data issue: false hasContentIssue false

A novel approach to runway overrun risk assessment using FRAM and flight data monitoring

Published online by Cambridge University Press:  20 May 2024

C. Reiser*
Affiliation:
Embraer SA, São Jose dos Campos, Brazil
E. Villani
Affiliation:
ITA: Instituto Tecnologico de Aeronautica, Brazil
M. Machado Cardoso-Junior
Affiliation:
ITA: Instituto Tecnologico de Aeronautica, Brazil
*
Corresponding author: C. Reiser; Email: christianne.reiser@embraer.com.br

Abstract

Runway overruns (ROs) are the result of an aircraft rolling beyond the end of a runway, which is one of the accident’s types that most frequently occurs on aviation. The risk of an RO arises from the synergistic effect among its precursors, such as unstable approaches, long touchdowns and inadequate use of deceleration devices. To analyse this complex socio-technical system, the current work proposes a customised functional resonance analysis method, called FRAM-FDM, as traditional techniques of risk and safety assessment do not identify the interactions and couplings between the various functional aspects of the system itself, especially regarding human and organisational components. Basically, FRAM-FDM is the coupling of a traditional FRAM with flight data monitoring (FDM) techniques, used here to quantify the variabilities of the flight crew performance while executing the required activity (i.e. the landing). In this proposal, these variabilities (i.e. the FRAM functions aspects) are aggregated by the addend of a logistic regression, resulting in a model to evaluate the flare operations and the brake application profile effect on the remaining distance to the end of the runway, used as a reference to classify the landing as acceptable or not. The present application of the FRAM-FDM assesses the operational risk of a sample fleet in overrunning the runway during landing, highlighting the brake pedal application profile as the most relevant contributor. The model improves the knowledge about the system behaviour, being useful to direct flight crew training.

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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

Flight Safety Foundation (FSF). ALAR Briefing Note 8.1: Runway Excursions, 2009. https://skybrary.aero/sites/default/files/bookshelf/865.pdf Google Scholar
Eurocontrol/Flight Safety Foundation. Global Action Plan for the Prevention of Runway Excursions (GAPPRE), 2021. https://www.skybrary.aero/sites/default/files/bookshelf/6046.pdf Google Scholar
Patriarca, R., Di Gravio, G., Woltjer, R., Costantino, F., Praetorius, G., Ferreira, P. and Hollnagel, E. Framing the FRAM: a literature review on the functional resonance analysis method. Saf. Sci., 2020, 129, pp 104827. doi: 10.1016/j.ssci.2020.104827 CrossRefGoogle Scholar
Yang, X., Yang, J., Zhang, Z., Ma, J., Sun, Y. and Liu, H. A review of civil aircraft arresting system for runway overruns. Prog. Aerosp. Sci., 2018, 102, pp 99121. doi: 10.1016/j.paerosci.2018.07.006 CrossRefGoogle Scholar
Benedetto, A., D’Amico, F. and Tosti, F. Improving safety of runway overrun through the correct numerical evaluation of rutting in cleared and graded areas. Saf. Sci., 2014, 62, pp 326338. doi: 10.1016/j.ssci.2013.09.008 CrossRefGoogle Scholar
Moretti, L., Di Mascio, P., Nichele, S. and Cokorilo, O. Runway veer-off accidents: quantitative risk assessment and risk reduction measures. Saf. Sci., 2018, 104, pp 157163. doi: 10.1016/j.ssci.2018.01.010 CrossRefGoogle Scholar
Kirkland, I.D.L., Caves, R.E., Humphreys, I.M. and Pitfield, D.E. An improved methodology for assessing risk in aircraft operations at airports, applied to runway overruns. Saf. Sci., 2004, 42, (10), pp 891905. doi: 10.1016/j.ssci.2004.04.002 CrossRefGoogle Scholar
Zhao, N. and Zhang, J. Research on the prediction of aircraft landing distance. J. Math. Prob. Eng., 2022. doi: 10.1155/2022/1436144 Google Scholar
Kang, Z., Shang, J., Feng, Y., Zheng, L., Wang, Q., Sun, H., Qiang, B. and Liu, Z. A deep sequence-to-sequence method for accurate long landing prediction based on flight data. IET Intell. Transport. Syst., 2021, 15, (8), pp 10281042. doi: 10.1049/itr2.12078 CrossRefGoogle Scholar
Tong, C., Yin, X., Wanga, S. and Zheng, Z. A novel deep learning method for aircraft landing speed prediction based on cloud-based sensor data. Fut. Generat. Comput. Syst., 2018, 88, pp 552558. doi: 10.1016/j.future.2018.06.023 CrossRefGoogle Scholar
Puranik, T., Rodriguez, N. and Mavris, D. Towards online prediction of safety-critical landing metrics in aviation using supervised machine learning. J. Transport. Res. Part C, 2020, 120. doi: 10.1016/j.trc.2020.102819 Google Scholar
Jacob, A., Lignée, R. and Villaumé, F. The runway overrun prevention system. Airbus Saf. Mag.: Saf. First, 8th Edition, 2009, pp 39.Google Scholar
Marques, C.C.A. Landing Distance Monitor. Applicant: Embraer S.A. Patent No.: US 10,453,349 B2, 2019.Google Scholar
Ayra, E.S., Ríos Insua, D. and Cano, J. Bayesian network for managing runway overruns in aviation safety. J. Aerosp. Inf. Syst., 2019, 16, (12), pp 546558. doi: 10.2514/1.I010726 Google Scholar
Calle-Alonso, F., Pérez, C.J. and Ayra, E.S. A Bayesian-network-based approach to risk analysis in runway excursions. J. Navigat., 2019, 72, (5), pp 119. doi: 10.1017/S0373463319000109 CrossRefGoogle Scholar
Barry, D.J. Estimating runway veer-off risk using a Bayesian network with flight data. J. Transport. Res. Part C, 2021, 128, p 103180. doi: 10.1016/j.trc.2021.103180 CrossRefGoogle Scholar
Li, C., Sun, R. and Pan, X. Takeoff runway overrun risk assessment in aviation safety based on human pilot behavioral characteristics from real flight data. Saf. Sci., 2023, 158. doi: 10.1016/j.ssci.2022.105992 CrossRefGoogle Scholar
Alnasser, H.H. and Czado, C. An application of D-vine regression for the identification of risky flights in runway overrun. Preprint, 2022. doi: 10.48550/arXiv.2205.04591 CrossRefGoogle Scholar
Reiser, C., Villani, E. and Machado-Cardoso, M. Jr Long touchdown through a safety-II perspective. Proceedings of the 33rd International Council of the Aeronautical Sciences (ICAS), 2022, 9, pp 67426755.Google Scholar
Wang, L., Ren, Y. and Wu, C. Effects of flare operation on landing safety: a study based on ANOVA of real flight data. Saf. Sci., 2018, 102, pp 1425. doi: 10.1016/j.ssci.2017.09.027 CrossRefGoogle Scholar
Oliveira, D., Moraes, A., Machado-Cardoso, M. Jr and Marini-Pereira, L. Safety analysis of RNP approach procedure using fusion of FRAM model and Bayesian belief network. J. Navigat., 2023, 76, (2–3), pp 286315. doi: 10.1017/S0373463323000152 CrossRefGoogle Scholar
Adriaensen, A., Patriarca, R., Smoker, A. and Bergström, J. A socio-technical analysis of functional properties in a joint cognitive system: a case study in an aircraft cockpit. Ergonomics, 2019, 62, pp 148. doi: 10.1080/00140139.2019.1661527 CrossRefGoogle Scholar
Patriarca, R., Del Pinto, G., Di Gravio, G. and Costantino, F. FRAM for systemic accident analysis: a matrix representation of functional resonance. Int. J. Reliab. Qual. Saf. Eng., 2018, 25, 1. doi: 10.1142/S0218539318500018 Google Scholar
Patriarca, R., Di Gravio, G. and Costantino, F. A Monte Carlo evolution of the Functional Resonance Analysis Method (FRAM) to assess performance variability in complex systems. Saf. Sci., 2017, 91, pp 4960. doi: 10.1016/j.ssci.2016.07.016 CrossRefGoogle Scholar
Yang, Q., Tian, J. and Zhao, T. Safety is an emergent property: illustrating functional resonance in air traffic management with formal verification. Saf. Sci., 2017, 93, pp 162177. doi: 10.1016/j.ssci.2016.12.006 CrossRefGoogle Scholar
International Air Transport Association (IATA). 2021 Safety Report, 2022. https://www.iata.org/contentassets/bd3288d6f2394d9ca3b8fa23548cb8bf/iata_safety_report_2021.pdf Google Scholar
Flight Safety Foundation (FSF). ALAR Briefing Note 7.1: Stabilized Approach, 2009. https://www.skybrary.aero/sites/default/files/bookshelf/864.pdf Google Scholar
Flight Safety Foundation (FSF). ALAR Briefing Note 8.3: Landing Distances, 2009. https://www.skybrary.aero/sites/default/files/bookshelf/867.pdf Google Scholar
Machado, F.M. Trajectory reconstruction tool for investigation of runway overruns. Biblioteca Digital Brasileira de Teses e Dissertações, 2009. https://bdtd.ibict.br/vufind/Record/ITA_014d8fc9e7ff275af9e39f618c44acf5 Google Scholar
European Cockpit Association (ECA). Pilot’s best practices for the prevention of runway excursions, 2022. https://skybrary.aero/sites/default/files/bookshelf/32704.pdf Google Scholar
Flight Safety Foundation (FSF). ALAR Briefing Note 8.4: Braking Devices, 2009. https://skybrary.aero/sites/default/files/bookshelf/868.pdf Google Scholar
Federal Aviation Administration (FAA). Advisory Circular (AC) No 91-79A: mitigating the risks of a runway overrun upon landing, 2016. https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_91-79A.pdf Google Scholar
Flight Safety Foundation (FSF). Reducing the risk of runway excursions – report of the runway safety initiative, 2009. https://flightsafety.org/files/RERR/fsf-runway-excursions-report.pdf Google Scholar
Hollnagel, E. FRAM: The Functional Resonance Analysis Method: Modelling Complex Socio-Technical Systems: Ashgate, 2012. doi: 10.1201/9781315255071 Google Scholar
Tian, W. and Caponecchia, C. Using the Functional Resonance Analysis Method (FRAM) in aviation safety: a systematic review. J. Adv. Transport., 2020. http://doi.org/10.1155/2020/8898903CrossRefGoogle Scholar
EASA. Guidance for the Implementation of Flight Data Monitoring Precursors. 3rd Revision, 2020. https://www.easa.europa.eu/sites/default/files/dfu/study_wgb_precursors_rev3_20200930_4.pdf Google Scholar
Delhom, J. Flight Data Analysis (FDA), a predictive tool for safety management system (SMS). Airbus Saf. Mag.: Saf. First, 17th Edition, 2014, pp 1518.Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing, 2021. https://www.R-project.org Google Scholar
Schwaiger, F. and Holzapfel, F. Fast decoding of ARINC 717 flight data recordings. Proceedings of AIAA SciTech 2021 Forum, 2021. http://doi.org/10.2514/6.2021-1982 CrossRefGoogle Scholar
Montgomery, D.C. and Runger, G.C. Applied Statistics and Probability for Engineers. New York, USA: John Wiley & Sons, 2003.Google Scholar
Battisti, I.D.E. and Smolski, F.M.S. Software R: Curso Avançado, 2019. https://smolski.github.io/livroavancado/reglog.html Google Scholar
Fernihough, A. Mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs, 2019. https://CRAN.R-project.org/package=mfx Google Scholar
Barbosa, A.M., Real, R., Munoz, A.R. and Brown, J.A. New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity Distrib., 2013, 19, (10), pp 13331338. https://onlinelibrary.wiley.com/doi/full/10.1111/ddi.12100.CrossRefGoogle Scholar