Review on EGT Prediction at Take-off

Exhaust Gas Temperature (EGT) serves as a vital indicator in jet engine health monitoring, particularly during highpower phases such as takeoff. Accurate prediction and monitoring of EGT can play a crucial role in ensuring engine efficiency, preventing overheating, and supporting predictive maintenance strategies. This review explores various data-driven approaches for EGT prediction, emphasizing the application of machine learning techniques such as linear regression. The integration of flight parameters like thrust and airspeed enables more accurate estimations of EGT behavior under dynamic conditions. In addition, visualization tools such as Google Charts and Python-based data analysis platforms (e.g., pandas, scikit-learn, and matplotlib) are discussed for their effectiveness in presenting real-time predictions and alerts. The paper also highlights how web-based simulations and alert systems can improve the interpretability and responsiveness of EGT monitoring frameworks. This review aims to provide a comprehensive understanding of EGT prediction technologies and their implications for enhancing modern aircraft engine diagnostics.