Abstract
This paper addresses a new data analysis method which is suitable to cluster flight data and complement current exceedance-based flight data monitoring programmes within an airline. The data used for this study consists of 296 simulated approaches from 4.5 NM to 1 NM to the runway threshold, flown by 74 participants (both pilots and non-pilots) with either a conventional sidestick or a gamepad in the future flight simulator at Cranfield University. It was clustered and analysed with the use of Kohonen’s Self-Organising Maps (SOM) algorithm. The results demonstrate that SOM can be a meaningful indicator for safety analysts to accurately cluster both optimal and less-optimal flying performance. This methodology can therefore complement current deviation-based flight data analyses by highlighting day-to-day as well as exceptionally good performance, bridging the cap of current analyses with safety-II principles.
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References
ICAO: Annex 6 Part 1 (2010). Accessed 27 Aug 2021. https://store.icao.int/en/annex-6-operation-of-aircraft-part-i-international-commercial-air-transport-aeroplanes
Li, W.-C., Nichanian, A., Lin, J., Braithwaite, G.: Investigating the impacts of COVID-19 on aviation safety based on occurrences captured through Flight Data Monitoring. Ergonomics, 1–39 (2022). https://doi.org/10.1080/00140139.2022.2155317
Flight Safety Foundation: Learning From All Operations: Expanding the Field of Vision to Improve Aviation Safety, July 2021. Accessed 18 Nov 2021. https://flightsafety.org/wp-content/uploads/2021/07/Learning-from-All-Operations-FINAL.pdf
Alreshidi, I., Moulitsas, I., Jenkins, K.W.: Advancing aviation safety through machine learning and psychophysiological data: a systematic review. IEEE Access 12, 5132–5150 (2024). https://doi.org/10.1109/ACCESS.2024.3349495
Walker, G.: Redefining the incidents to learn from: safety science insights acquired on the journey from black boxes to Flight Data Monitoring. Saf. Sci. 99, 14–22 (2017). https://doi.org/10.1016/j.ssci.2017.05.010
Maille, N.: On the use of flight operating procedures for the analysis of FOQA data. In: 6th European Conference for Aeronautics and Space Sciences (EUCASS), Krakow: ONERA, July 2015. Accessed 9 Nov 2021. https://www.researchgate.net/publication/314973879
Bromfield, M.A., Landry, S.J.: Loss of control in flight – time to re-define? In: AIAA Aviation 2019 Forum, pp. 1–10 (2019). https://doi.org/10.2514/6.2019-3612
Chan, W., Li, W.-C.: Perception of causal factors in flight operations between Ab-Initio and expatriate pilots. In: 2023 7th International Conference on Transportation Information and Safety (ICTIS), pp. 1728–1732. IEEE, August 2023. https://doi.org/10.1109/ICTIS60134.2023.10243970
Jasra, S.K., Valentino, G., Muscat, A., Camilleri, R.: Hybrid machine learning-statistical method for anomaly detection in flight data. Appl. Sci. 12(20), 10261 (2022). https://doi.org/10.3390/app122010261
Hollnagel, E.: Safety–I and Safety–II, 1st edn. CRC Press, London (2018). https://doi.org/10.1201/9781315607511
Oehling, J., Barry, D.J.: Using machine learning methods in airline flight data monitoring to generate new operational safety knowledge from existing data. Saf. Sci. 114, 89–104 (2019). https://doi.org/10.1016/j.ssci.2018.12.018
Li, W.-C., Wang, Y., Korek, W.T.: To be or not to be? Assessment on using touchscreen as inceptor in flight operation. Transp. Res. Procedia 66, 117–124 (2022). https://doi.org/10.1016/j.trpro.2022.12.013
Korek, W.T., Li, W.C., Lu, L., Lone, M.: Investigating pilots’ operational behaviours while interacting with different types of inceptors. In: Harris, D., Li, WC. (eds.) HCII 2022, vol. 13307. LNAI, pp. 314–325. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06086-1_24
Mitchell, T.M.: Machine Learning. McGraw-Hill Science/Engineering/Math (1997)
Barry, D.J.: Estimating runway veer-off risk using a Bayesian network with flight data. Transp. Res. Part C Emerg. Technol. 128 (2021). https://doi.org/10.1016/j.trc.2021.103180
Das, S., Li, L., Srivastava, A.N., John Hansman, R.: Comparison of algorithms for anomaly detection in flight recorder data of airline operations. In: 12th AIAA Aviation Technology, Integration and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. American Institute of Aeronautics and Astronautics Inc. (2012). https://doi.org/10.2514/6.2012-5593
Li, L., Das, S., Hansman, R.J., Palacios, R., Srivastava, A.N.: Analysis of flight data using clustering techniques for detecting abnormal operations. J. Aerosp. Inf. Syst., 587–598 (2015). https://doi.org/10.2514/1.I010329
Fernández, A., et al.: Flight Data Monitoring (FDM) unknown hazards detection during approach phase using clustering techniques and AutoEncoders. In: 9th SESAR Innovation Days. SESAR (2019)
Krogh, A.: What are artificial neural networks? Nat. Biotechnol. 26(2), 195–197 (2008). https://doi.org/10.1038/nbt1386
Bação, F., Lobo, V., Painho, M.: Self-organizing maps as substitutes for K-means clustering. In: Sunderam, V.S., van Albada, GDick, Sloot, P.M.A., Dongarra, J. (eds.) Computational Science – ICCS 2005. LNCS, vol. 3516, pp. 476–483. Springer, Heidelberg (2005). https://doi.org/10.1007/11428862_65
Bardet, J.-M., Faure, C., Lacaille, J., Olteanu, M.: Design aircraft engine bi-variate data phases using change-point detection method and self-organizing maps. In: Grenada: ITISE, September 2017. Accessed 18 Jan 2024. https://www.safran-aircraft-engines.com
Bektas, O.: Visualising flight regimes using self-organising maps. Aeronaut. J. 127(1316), 1817–1831 (2023). https://doi.org/10.1017/AER.2023.71
Andrades, I.S., Castillo Aguilar, J.J., García, J.M.V., Carrillo, J.A.C., Lozano, M.S.: Low-cost road-surface classification system based on self-organizing maps. Sensors 20(21), 6009 (2020). https://doi.org/10.3390/s20216009
Flight simulator at Cranfield University wins international award. Accessed 31 Jan 2024. https://www.cranfield.ac.uk/press/news-2021/flight-simulator-at-cranfield-university-wins-international-award
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586–600 (2000). https://doi.org/10.1109/72.846731
Shahapure, K.R., Nicholas, C.: Cluster quality analysis using silhouette score. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 747–748. IEEE, October 2020. https://doi.org/10.1109/DSAA49011.2020.00096
Ayadi, T., Hamdani, T.M., Alimi, A.M.: A new data topology matching technique with multilevel interior growing self-organizing maps. In: 2010 IEEE International Conference on Systems, Man and Cybernetics, pp. 2479–2486. IEEE, October 2010. https://doi.org/10.1109/ICSMC.2010.5641936
Bauer, H.-U., Pawelzik, K.R.: Quantifying the neighborhood preservation of self-organizing feature maps. IEEE Trans. Neural Netw. 3(4), 570–579 (1992). https://doi.org/10.1109/72.143371
De Bodt, E., Cottrell, M., Verleysen, M.: Statistical tools to assess the reliability of self-organizing maps. Neural Netw. 15(8–9), 967–978 (2002). https://doi.org/10.1016/S0893-6080(02)00071-0
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Nichanian, A., Li, WC., Korek, W.T., Wang, Y., Chan, W.TK. (2024). Self-organising Maps for Comparing Flying Performance Using Different Inceptors. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. HCII 2024. Lecture Notes in Computer Science(), vol 14693. Springer, Cham. https://doi.org/10.1007/978-3-031-60731-8_8
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