Analysis of the Aviation Safety Management System by Fractal and Statistical Tools

• Autorzy: Bugayko Dmytro , Leshchynskyi Оleg , Sokolova Nataliya , Isaienko Volodymyr , Zamiar Zenon

Identyfikatory

DOI

10.26411/83-1734-2015-4-44-5-19

• Języki publikacji: EN

Abstrakty

EN

World civil aviation is an open-source system that is affected by a large number of related and non-related factors. Aviation safety is one of the prioritized directions in the industry. Its managerial decision-making process is primarily based on a versatile analysis of security data in which the choice of the appropriate mathematical apparatus is fundamental. This article suggests applying fractal-statistical analysis to evaluate the aviation safety management system in terms of determining the random distribution of quantitative dynamics of aircraft crashes with lethal consequences in the period from 1946 to 2017. This allows us to verify the adequacy of probabilistic approaches appliance in analysing the dynamics of aviation disasters. The results of research carried out on the basis of the Hurst exponent have allowed us to conclude that the dynamics of aviation disasters is characterized by the effect of "spatial memory". In other words, these are "hidden laws", for which further investigation can become an effective tool for the development of proactive methods in managing aviation safety.

Słowa kluczowe

EN

aviation safety; data analysis; fractal-statistical analysis; Hurst exponent

• Wydawca: Międzynarodowa Wyższa Szkoła Logistyki i Transportu we Wrocławiu
• Czasopismo: Logistics and Transport
• Rocznik: 2019
• Tom: Vol. 44, No. 4
• Strony: 41--59
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Bugayko Dmytro

• National Aviation University Kiev, Ukraine

autor

Leshchynskyi Оleg

• National Aviation University Kiev, Ukraine

autor

Sokolova Nataliya

• National Aviation University Kiev, Ukraine

autor

Isaienko Volodymyr

• National Aviation University Kiev, Ukraine The Higher School Academy of Sciences of Ukraine

autor

Zamiar Zenon

• The International University of Logistics and Transport in Wroclaw, Poland

Bibliografia

• [1] Safety Management System, Handbook Doc 9859, Fourth Edition, ICAO, Montreal 2018.
• [2] The Convention on International Civil Aviation Doc 7300, signed in Chicago on December 7, 1944, ICAO, Montreal 2006.
• [3] Annex 19 to the Convention on the International Civil Aviation Organization, Safety Management System, ICAO, Montreal 2016.
• [4] Kharchenko V., Paweska M., Bugayko D., Antonova A., Grigorak M., Theoretical Approaches for Safety Levels Measurements – Sequential Probability Ratio Test (SPRT), “Logistics and Transport”, 34 (2017)/2, pp. 25–31.
• [5] Kharchenko V., Bugayko D., Modern Trends of Aviation Logistics Development – Effectiveness, Safety and Security Aspects, “Logistics and Transport”, 18 (2013)/2, pp. 17–23.
• [6] Hak-Tae L., Meyn L. A., Kim S. Y., Probabilistic Safety Assessment of Unmanned Aerial System Operations, “Journal of Guidance, Control and Dynamics”, 36 (2013)/2, pp. 610-617.
• [7] Kharchenko V., Chynchenko Y., Integrated safety management system in air traffic services, „Proceedings of the National Aviation University”, 58 (2014)/1, pp. 6–10.
• [8] Borsuk S., Reva O., Kharchenko V., Multiplication of Air Accidents Frequency and Hazard Desirability Coefficients for ICAO Safety Risk Tolerability Matrix Solution, “Logistics and Transport”, 25 (2015)/1, pp. 63- 70.
• [9] Aref'eva O. V., Oleshko T. I., Marusich O. V., Leshchynsky O. L., Quasicyclical pre-forecast analysis of world oil prices, [in:] Scientific proceedings: a collection of scientific articles, NAU, Kiev 2011, pp. 25-31.
• [10] Borisenko Y. G., Groza V. A., Leschinsky O. L., Fractal-statistical analysis of fluctuations of Trubiz river water levels, “Knowledge Technology”, (2014), pp. 21-25.
• [11] Demidova L. A., Pyilkin A. N., Skvortsov S. V., SkvortsovaT. S., Gibridnyie modeli prognozirovaniya korotkih vremennyih ryadov, Goryachaya liniya, Telekom, 2015.
• [12] Aviation Safety Network (ASN) - https://aviation-safety.net/statistics
• [13] Kalush Yu. A., Pokazatel Hersta i ego skryityie svoystva, “Sib. zhurn. industr. Matem.”, 5 (2002)/4, pp. 29–37.
• [14] Feder E., Fraktaly, M. Mir, s. l. 1991.
• [15] Kirichenko L. O., Sravnitelnyiy analiz metodov otsenki parametra Hersta samopodobnyih protsessov, „Sistemi obrobki Informatsyi”, 48 (2005)/8, pp. 113-117.
• [16] Beran J., Statistics for long-memory process, Chapman&Hall, New York 1994.
• [17] Pratsevity M. V., A fractal approach in singular distribution studies, NPU MP Drahomanov, Kiev 1998.
• [18] Hurst H. E., Long-term storage capacity of reservoirs, “Transactions of the American Society of Civil Engineers”, 116 (1951)/5, pp. 770-808.
• [19] Byistray G. P., Korshunov L. A., Nikulin N. L., Lyikov I. A., Diagnostika i prognozirovanie sotsialno-ekonomicheskogo razvitiya regionov v ramkah nelineynoy dinamiki, “Vestnik Tyumenskogo Gosudarstvennogo Universiteta”, (2010)/4, pp. 164-170.
• [20] Nagornov O. V., Nikitaev V. G., Veyvlet-analiz v primerah: ucheb.posobie, M.: Izd-vo NIYaU MIFI, 2010.