Explainable AI-based Shapley Additive Explanations for Remaining Useful Life Prediction using NASA Turbofan Engine Dataset

Artificial intelligence (AI) models can provide valuable predictions of remaining useful life (RUL), but it is difficult to understand how these ‘black box’ models come to their conclusions. Explainable AI (XAI) seeks ways to make the inner workings of these models more understandable, which in turn may assist in system development and adoption. Here we use SHAP analyses to visualize and interpret interactions between top performing parameters in a high-accuracy neural network model of RUL in a simulated fleet of NASA turbojet engines. Our analyses reveal complex relationships between even the best performing parameters and demonstrate a method for increasing explainability of an AI model by decomposing parameter relationships to better understand how parameter interactions influence model performance.