Convolutional Neural Networks for Gas Turbine Exhaust Gas Temperature and Power Predictions

Abstract

In this work, a data-driven and deep learning-based predictive modeling framework has been developed for generating accurate prediction models intended for gas turbine engine performance analysis. This paper focuses on the application of Convolutional Neural Networks (CNNs) along with tabular data to image conversion techniques to predict exhaust gas temperature (EGT) and power outputs of Gas Turbine Engines (GTE). Using one such tabular data to image conversion method called Image Generator for Tabular Data (IGTD), several CNN model architectures were explored, and their predictive capabilities were compared. The effectiveness of the proposed predictive modeling framework which combines CNNs and the IGTD algorithm has been demonstrated for EGT and power prediction using GTE operational data collected over a period of three years. The CNN models using images converted from tabular data exhibit superior predictive capabilities for both EGT and power, with a more significant improvement observed for EGT prediction. To the best of our knowledge, this is the first attempt to apply IGTD based CNNs for developing GTE models for EGT and power prediction.