Explainable Artificial Intelligence for the Remaining Useful Life Prognosis of the Turbofan Engines
Chang Woo Hong, Changmin Lee, Kwangsuk Lee, Min-Seung Ko, Kyeon Hur
Abstract
This paper proposes a deep-stacked neural network to prognose the remaining useful life of the turbofan engines and analyze results using explainable artificial intelligence. The proposed model prognoses the remaining useful life of the turbofan engines accurately by properly stacking a one-dimensional convolutional neural network (1D-CNN), long short-term memory (LSTM), and bidirectional LSTM algorithms. This model also uses dropout technique and residual network to enhance the prediction accuracy. The Explainable artificial intelligence analyzes the prognostic results of RUL. Using SHAP (SHapely Addictive exPlanation), this model can analyze features that have significantly influenced RUL prediction. The SHAP force plot and decision plot can help decision-makers of the turbofan engine properly manage the maintenance by showing the influence of sensors.