Gas Turbine Vibration Detection and Identification based on Dynamic Artificial Neural Networks

Abstract

Vibration control in rotating machinery is a major challenge in oil and gas facilities that use these machines. In gas turbines, the instability phenomenon is generated by the rotor, and measurements must be made in the axial plane of the turbine. Minor defects can lead to significant vibration amplifications, making it imperative to detect these defects early. The goal of this study is to develop a diagnostic strategy to monitor faults affecting a turbine system using a supervision approach based on artificial neural networks. This strategy allows for early detection of faults, which allows for efficient management of vibration-induced failures, as well as economic gain, by recovering the transported gas used in these machines. By describing the vibration-related parameters and representing the state of the vibratory motion, the proposed approach provides a powerful tool for vibration control in rotating machines.