Integrating Network Theory and SHAP Analysis for Enhanced RUL Prediction in Aeronautics

The prediction of Remaining Useful Life (RUL) in aerospace engines is a challenge due to the complexity of these systems and the often-opaque nature of machine learning models. This opaqueness complicates the usability of predictions in scenarios where transparency is crucial for safety and operational decision-making. Our research introduces the machine learning framework that significantly improves both the interpretability and accuracy of RUL predictions. This framework incorporates SHapley Additive exPlanations (SHAP) with a surrogate model and Network Theory to clarify the decision-making processes in complex predictive models and enhance the understanding of the hidden pattern of features interaction. We developed a Feature Interaction Network (FIN) that uses SHAP values for node sizing and SHAP interaction values for edge weighting, offering detailed insights into the interdependencies among features that affect RUL predictions. Our approach was tested across 44 engines, showing RMSE values between 2 and 17 and NASA Scores from 0.2 to 1.5, indicating an increase in prediction accuracy. Furthermore, regarding interpretability the application of our FIN, revealed significant interactions among corrective speed and critical temperature points key factors in engine efficiency and performance.