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THEORETICAL BASIS OF THE USE OF NEURAL NETWORKS FOR DIAGNOSING DAMAGE TO AIRCRAFT EQUIPMENT IN THE FAR NORTH

Struchkova Anna Mikhailovna  (Candidate of Technical Sciences, Associate Professor, NEFU named after. M.K Ammosova; JSC "Airline "Yakutia" (Yakutsk, Russia) )

This article provides a comprehensive analysis of the theoretical foundations for using artificial neural networks to diagnose aircraft damage in the Far North. Specific factors affecting aircraft damage during operation in Arctic conditions are examined. Adapted neural network architectures used to solve predictive maintenance problems in extreme climates are analyzed. Particular attention is paid to the problems of diagnosing corrosion damage, material degradation at low temperatures, and the specifics of data processing in communication-limited environments. The results of the study demonstrate the significant potential of neural network technologies for improving the safety and cost-effectiveness of aircraft operation in the Far North.

Keywords:neural networks, damage diagnosis, aircraft, Far North, Arctic conditions, predictive maintenance, low-temperature diagnostics, control automation.
 

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Citation link:
Struchkova A. M. THEORETICAL BASIS OF THE USE OF NEURAL NETWORKS FOR DIAGNOSING DAMAGE TO AIRCRAFT EQUIPMENT IN THE FAR NORTH // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2026. -№01. -С. 151-157 DOI 10.37882/2223-2966.2026.01.30
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