Helicopter Turboshaft Engines' Neural Network System for Monitoring Sensor Failures.

An effective neural network system for monitoring sensors in helicopter turboshaft engines has been developed based on a hybrid architecture combining LSTM and GRU. This system enables sequential data processing while ensuring high accuracy in anomaly detection. Using recurrent layers (LSTM/GRU) is critical for dependencies among data time series analysis and identification, facilitating key information retention from previous states.

Transient Electromagnetic Processes Analysis in High Voltage Transmission Lines during Two-Phase Short Circuits.

The mathematical model of a fragment of a high-voltage electric network is developed in this paper. The network consists of a long power line with distributed parameters and an equivalent three-phase active-inductive load. Neumann and Robin-Poincare boundary conditions were used to identify the boundary conditions for the long line equation.