ON THE EXPERIMENTAL SETUP FOR APPROBATION OF AN ALGORITHM FOR PROCESSING DIAGNOSTIC PARAMETERS OF AIRCRAFT GAS TURBINE ENGINE BASED ON MULTILAYER NEURAL NETWORKS

The paper presents experimentally substantiated tabular data for hyperparameter tuning of multilayer neural networks in aviation gas turbine engine diagnostics. The authors propose seven original algorithms for adaptive training parameter tuning, including methods for dynamic adaptation of the learning rate, strategies for changing the network architecture depending on the engine operating mode, and adaptive approaches to regularization. The parameter ranges cover values from 10–5 to 103, which ensures practical applicability for various architectures and data types. The scientific novelty lies in the creation of adaptive algorithms that take into account the specifics of the diagnostic parameters of gas turbine engine components and their time dynamics.