Evolving cloud-based fault monitoring system for an industrial gas turbine

Abstract

The Thesis focuses on the development and implementation of an evolving fault monitoring system for an industrial gas turbine. Building on our study of turbine during our internship, this work is dedicated to creating an evolving cloud-based system of AnYa-type to detect and identify different fault scenarios in the industrial gas turbine. This model leverages real-time data and non-parametric methods to adapt to dynamic environments, enhancing efficiency and reliability in complex systems. Our efforts have been directed towards building this monitoring system to ensure continuous learning and adaptation, ultimately improving operational performance under normal and fault modes.