Predictive maintenance of aircraft engines using machine learning: a case of the CFM56-7B26E engine

Abstract

Understanding the Exhaust Gas Temperature margin is essential for predictive maintenance of aircraft engines. It addresses the challenges of unscheduled maintenance and equipment downtime. Since aircraft engines are expensive to purchase and maintain, airline operators strive to maximize the use of installed engines. This project used several machine learning models to predict the EGT Hot Day Margin. A real CFM56-7B26E engine dataset is used for analysis and prediction. The models were evaluated using metrics such as mean absolute error, mean square error and root mean square error. Results showed that the deep learning model performed better than all the other regression algorithms, and cross-validation against engine operational limits achieved an accuracy of 98%. SHAP analysis further identified the most relevant features influencing the model predictions. The key factors include EGT, indicated fan Speed, total air temperature, core speed, mach, and the Operational length of the engine, highlighting their significant impact on engine performance. The best-performing model was then deployed on the Streamlit community cloud platform. This project demonstrates how machine learning can enhance the reliability of aircraft engine maintenance practices.

Keywords: Exhaust Gas Temperature (EGT), Predictive Maintenance, Aircraft Engine, Machine Learning.