Neural Network Diagnosis Model for a Small-sized Gas Turbine Engine

Authors

  • Sukhanov Andrey Vladimirovich Ufa University of Science and Technology
  • Zyryanov Alexey Viktorovich Ufa University of Science and Technology
  • Tsypaev Nikita Denisovich Ufa University of Science and Technology
  • Karimov Emil Shamilevich Ufa University of Science and Technology

Keywords:

gas turbine engine, parametric diagnostics, neural networks, small gas turbine engine

Abstract

A neural network model is applied to the diagnostics of the SR-30 small gas turbine engine. A mathematical model was developed based on thermogasdynamic calculations and verified using GasTurb software and bench testing data. The resulting dependencies were used to train a multilayer perceptron, which classifies the engine's condition based on current parameters. The SR-30 Engine Simulator simulates turbine operating processes and degradation by reducing its efficiency. The results demonstrate that the neural network model is capable of detecting parameter deviations, confirming the feasibility of this approach for early diagnostics and improving the reliability of small gas turbine engines. doi 10.54708/19926502_2026_3031133

Author Biographies

Sukhanov Andrey Vladimirovich, Ufa University of Science and Technology

Senior Lecturer at the Department of Aviation Engines.

Zyryanov Alexey Viktorovich, Ufa University of Science and Technology

Can. Sci. in electric jet engines and power installations of the aircraft (Ufa State Aviation Technical University, 2008). Head of the Department of Aviation Engines, Associate Prof.

Tsypaev Nikita Denisovich, Ufa University of Science and Technology

Master’s degree in technical operation of aircraft and engines (Ufa University of Science and Technology, 2025)

Karimov Emil Shamilevich, Ufa University of Science and Technology

Student at the Department of Aviation Engines

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Published

2026-07-10

Issue

Vol. 30 No. 3(113) (2026)

Section

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