Using machine learning to process flight information

The concept of maintaining a high level of flight safety in civil aviation implies reducing the total number of aviation incidents and accidents on commercial flights due to technical malfunctions of the aircraft. Today, minimizing the human factor in the field of aircraft maintenance and repair, as well as system failures, can be achieved by implementing machine learning in specialized programs for flight information processing units. This article is devoted to the improvement of the flight information department's program through the implementation of software, which, in turn, will accelerate the process of finding and preventing defects by testing the state of the aircraft related systems. To verify the proposed method, the article calculates statistical flight safety indicators. The calculation shows that for a Boeing 777-300ER aircraft, the probability of a certain number of flights being completed safely will increase from e⁻⁷⁷ to e⁻⁷⁵ . The results demonstrate an increase in the accident-free flight rate, which is a key objective of the implemented software.

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