Flight safety level improvement methodology based on the pilot model

Despite the regular efforts on the part of national regulators, the International Civil Aviation Organization and the International Air Transport Association (ICAO and IATA), as well as on the developers of aeronautical equipment, the vast majority of accidents and incidents continue to occur due to the human factor. With the course of time, aircraft design and reliability are steadily and significantly improving, nevertheless, the number of aviation accidents is happening more and more frequently, including accidents with serviceable aircraft. Considerable evidence is the fact that a Controlled Flight into Terrain (CFIT) remains one of the most common causes of aviation accidents. This is specified by a wide variety of problems that require the search for complex, interconnected solutions. Among these issues it is necessary to highlight the increasing sophistication of the aircraft as a technical system, as well as practically unchanged for more than half a century approaches to pilots training for the type and maintaining their qualifications based on pre-defined scenarios taking into consideration the previous experience of aircraft operation. One of the possible ways out of the situation may be the introduction of so-called concept of personnel training relying on the evidence-based training analysis (EBT) based not on the pursue to memorize a certain list of exercises but to develop each particular pilot’s skills and competences that could help him cope with any unpredictable situation. The key feature of EBT lies in refocusing on the analysis of original causes of unsuccessful maneuvers (actions of the pilot) primarily in order to correct the wrong actions instead of repeatedly complying with the "correct sequence of actions". In this regard, the tools providing a continuous analysis of the pilot's actions to identify errors for the purpose of realigning (forming) the pilot's professional competencies in due time, are of paramount importance. The article describes the content of the methodology representing an ultimate goal to develop recommendations aimed at improving pilot’s expertise based on generalized and personalized models of the pilot, as well as solving the inverse problem of flight dynamics using a comparative assessment of a particular pilot piloting quality.

1. Kelly, D. and Efthymiou, M. (2019). An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017. Journal of Safety Research, vol. 69, pp. 155–165. DOI: 10.1016/j.jsr.2019.03.009

2. Krikunov, K.N. (2013). Problems of training of commercial aviation pilots. Bulletin of the South Ural State University. Series: Education. Educational Sciences, vol. 5, no. 2, pp. 79–87. (in Russian)

3. Bolshedvorskaya, L.G. and Miroshnichenko, A.A. (2009). Economic and methodological aspects of simulator training of aircraft crew aircraft grew members. Nauchnyy Vestnik MGTU GA, no. 143, pp. 55–62. (in Russian)

4. Krikunov, K.N. (2013). To the question of problems with training pilots of civil aviation. Bulletin of the South Ural State University. Series: Education. Educational Sciences, vol. 5, no. 1, pp. 147–150. (in Russian)

5. Karpova, L.I. and Nikitin, D.A. (2011). The training of civil aviation personnel is a pledging of security of air transportation. Nauchnyy Vestnik MGTU GA, no. 166, pp. 102–106. (in Russian)

6. Petrunin, S.V. and Bolshedvorskaya, L.G. (2011). The organization of training of flight crew members by airlines depending on the program complexity. Nauchnyy Vestnik MGTU GA, no. 167, pp. 21–26. (in Russian)

7. Moroney, W.F. and Moreney, B.W. (1999). Flight simulation. In: handbook of aviation human factors, pp. 355–388.

8. Tikhiy, I.I., Kashkovsky, V.V. and Poluektov, S.P. (2009). Estimation of quality of piloting in the mode flight on glide path. Nauchnyy Vestnik MGTU GA, no. 138, pp. 191–197. (in Russian)

9. Evdokimenkov, V.N., Kim, R.V. and Jakimenko, V.A. (2016). Technical and biological parts of ergatic system "pilot-aircraft" accommodation using artificial neural network approach. Trudy MAI, no. 89. 21 p. Available at: http://trudymai.ru/upload/iblock/eaa/evdokimenkov_kim_yakimenko_rus.pdf?lang=ru&issue=89 (accessed: 21.02.2021). (in Russian)

10. Gladkov, B.M. (1991). Avtomatizirovannaya otsenka natrenirovannosti letchikov s ispolzovaniyem pokazateley upravlyayushchikh vozdeystviy [Automated training evaluation of pilots using control actions indicators]. Nauchno-metodicheskiye materialy po problemam obespecheniya bezopasnosti poletov. Irkutskoye VVAIU, pp. 73–79. (in Russian)

11. Rudnyy, N.M., Kopanev, V.I., Chernyakov, I.N. and others. (1986). Aviatsionnaya meditsina [Aviation medicine], in Rudnyy N.M. (Ed.). Moscow: Meditsina, 577 p. (in Russian)

12. Frolov, N.I. (1978). Puti izucheniya rabotosposobnosti letchika v polete [Ways to study the pilot flight performance]. Kosmicheskaya biologiya i aviakosmicheskaya meditsina, vol. 12, no. 1, pp. 3–11. (in Russian)

13. Poluehktov, S.P., Kashkovskij, V.V., Tikhij, I.I. and Lapin, I.P. (2011). Method of assessing quality of piloting aeroplane by pilot during landing phase based on data from standard onboard recording device. Patent RU, no. 2 436 164 C1, December 10, 2011. (in Russian)

14. Fikhtengolts, G.M. (2020). Kurs differentsialnogo i integralnogo ischisleniya: uchebnik dlya vuzov. V 3 tomakh. Tom 3 [Differential and integral calculus course: Textbook for Universities]. 11th ed., ster. Moscow: Lan, 656 p. (in Russian)

15. Ied, K., Maslennikova, G.E. and Tyumentsev, Ju.V. (2020). Computing safe parameters of maneuver commencing of aerobatics aircraft using artificial neural network. Aerospace MAI Journal, vol. 27, no. 2, pp. 169–184. DOI: 10.34759/vst-2020-2-169-184 (in Russian)

16. Vorobyev, V.V., Beliatskaya, A.P. and Supolka, A.A. (2020). Methodic aspects of aircraft glide slope correction for prevention of cfit category accidents during pre-landing descent. Civil Aviation High Technologies, vol. 23, no. 4, pp. 33–44. DOI: 10.26467/2079-0619-2020-23-4-33-44 (in Russian)

17. Haykin, S. (1999). Neural networks: a comprehensive foundation. 2nd ed. Hamilton, Ontario, Canada: Prentice Hall, 842 p.