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Civil aviation flight safety: pilot properties soft computing

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Commercial competition leads to aviation accidents. It forces airlines to reduce the cost of purchasing, leasing, and maintenance of aircraft. The air carrier saves on professional training of personnel, on an arbitrary increase in the workload standards, on the use of flight crews with minimal and untenable experience in chronic fatigue conditions. Theory and methods of the characteristics of specialists remain uncertain. Statistical data and expertise may be piecewise-defined, inaccurate, and inconsistent. It is necessary to establish indicators and values of acceptable accuracy using fuzzy measures to calculate the dependability of flight crews based on workload and experience. It is proposed soft computing, statistical and expert methods for calculating the properties of a person and social groups in the management of dangerous professions. It makes it possible to calculate the dependability of the pilot properties with an assessment of flight safety risk levels for making management decisions. The results of the work are new standards for the workload of flight crews recommended for civil aviation. Results are obtained in qualitative methods for calculating efficiency, security, and risk states in the management of organizational objects as airlines. Indicators for air transport risk management standards and decision-making tools were obtained. Calculated indicators of pilot dependability values are a model for developing the airline's strategy, for quantitative assessments of flight specialists, standardizing professional activities, and managing training costs.
Rocznik
Tom
Strony
5--14
Opis fizyczny
Bibliogr. 23 poz., tab.
Twórcy
  • Scientific Research Project Civil Aviation Institute «AviaManager» 630078 Novosibirsk, Russian Federation
Bibliografia
  • 1. Kane M.R. (1990). Air Transportation, USA, Iowa: Kendall Hunt Publishing Company, 500.
  • 2. Plotnikov N.I. (2018). The Development of the Subject Domain Observation Complex for Management Purposes, 14th International Scientific-Technical Conference APEIE – 44894, Vol. 1, Part 1, 268–272.
  • 3. Salthouse T. (1990). Influence of experience on age differences in cognitive functioning, Human Factors, No. 32, 551-569.
  • 4. Shappell S.A., Wiegmann D.A. (2001). The Human Factors Analysis and Classification System (HFACS), FSF Flight Safety Digest, No. 2, 15-28.
  • 5. Wiener E.L., Kanki B.G., Helmreich R.L. (1993). Cockpit Resource Management, USA. N.Y, Academic Press, 519.
  • 6. Klir G.J. (1990). Architecture of systems problem solving, М.: Radio and Communication, 544, (in Russian).
  • 7. Dubois D., Prade A. (1990). The theory of possibilities. Applications to knowledge representation in computer science. Transl. from French. - Moscow: Radio and communications, 288 (in Russian).
  • 8. Zadeh L.A. (1994). Fuzzy Logic, Neural Networks, and Soft Computing, Communications of the ACM, Vol. 37, No. 3, 77-84.
  • 9. Salvatore S. et al. (1986). Air transport pilot involvement in general aviation accidents, Ergonomics, Vol. 29, No. 11, 1455-1467.
  • 10. Aarons R.N. (1987). Safety statistics for prudent pilot, Business and Commercial Aviation, Vol. 61, No. 1. 54-57.
  • 11. Broach D., Joseph K.M., Schroeder D.J. (2003). Pilot age and accident rates report 3: an analysis of professional air transport pilot accident rates by age, Civil Aeromedical Institute, Human Resources Research Division, FAA, 63.
  • 12. Hancock P.A. (1988). Mental workload dynamics in adaptive interface design, IEEE Transactions on Systems, Man and Cybernetics, Vol. 18, No. 4, 647-658.
  • 13. Massie D. L., Campbell K.L., Williams A. F. (1995). Traffic accident involvement rates by driver age and gender, Accident Analysis and Prevention, No. 27, 73-87.
  • 14. Mortimer R.G. (2000). Differences in accidents of professional pilots and non-professional pilots as a means of identifying training needs. National Aviation Safety Data Analysis Center, Introduction to the databases. FAA // http://www.nasdac.faa.gov/internet/fw_learn.htm.
  • 15. Salthouse T. (1990). Influence of experience on age differences in cognitive functioning / T. Salthouse, Human Factors. No. 32, 551-569.
  • 16. Salvatore S., Stearns M.S., Huntley Jr., Mengert P. (1986). Air transport pilot involvement in general aviation accidents // Ergonomics. Vol. 29, No. 11, 1455-1467.
  • 17. Air Line Pilot. (1980). Vol. 48, No. 2, 15.
  • 18. Aviation Week and Space Technology. (1988). Vol. 129, No. 7, 42.
  • 19. Shaw R.R. Airline Safety 1950-2000 // Aircraft. (1985), No. 1, 32-34.
  • 20. CAST: Commercial Aviation Safety Team. Process for Conducting Joint Implementation Measurement And Data Analysis Teams (JIMDATs), DRAFT, June 2004.
  • 21. Li Guohua, Baker S. P., Grabowski J.G., Yandong Qiang, McCarthy M.L., Rebok G.W. (2003). Age, Flight Experience, and Risk of Crash Involvement in a Cohort of Professional Pilots, American Journal of Epidemiology, Vol. 157, No. 10, 874-880.
  • 22. Plotnikov N.I. (2013). Resurcy pilota. Nadezhnost. Monographia. [Pilot resources. Dependability. Monograph], Novosibirsk, Russia, AviaManager Publ., 264 (in Russian).
  • 23. Automated system for predicting and preventing accidents at the organization and production of air transport. The intermediate stage No. 4: Adaptation of the developed algorithms and software АS. Scientific- and-technical report (2012). No 194, 1340. NI. Plotnikov - Section 3. 154-238, Applications: I, К, L, М, N. 1048 - 1258 (in Russian).
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-af87da7e-3133-466c-9d84-3d277de88a00
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