Determining Aviation Equipment Malfunctions Type Influence on Neural Network Diagnostic Models Behavior

The polymorphic RBF network was proposed, which demonstrates the required precision in approximating nonlinear functions, as shown by its success in optimizing helicopter turboshaft engines' performance parameters. It trains quickly with minimal configuration and produces smoother fitting curves than other neural networks, aiding in trend analysis. Results indicate that the diagnostic neural model for engines with power loss shows a sharp decline to minimal values, suggesting a potential malfunction. In contrast, the model for a functioning engine shows smooth parameter variations throughout the observation period.