Control and diagnostic of vibration in gas turbine system using neural network approach

This paper presents an approach of rotating machinery fault diagnosis based on Nonlinear Autoregressive with External (Exogenous) Input NARX neural networks. This tool is trained on the real data obtained from the sensors at bearing and it is used to ensure the faults diagnosis of the most damages that can appear in the system of gas turbine. Indeed the artificial neural networks provide an effective method for fault diagnosis in terms of reliability of system and which allow to keep the optimal condition of exploitation. In this paper the real data obtained from the bearing of the twin shaft gas turbine GE 3002 is chosen to detect the vibration and to ensure the analysis of this vibration, where the main objective is the monitoring of the studied system.