Ensemble Learning for Remaining Useful Life Prediction

Abstract

While significant research has been conducted in modelbased and data-driven prognostics, very limited research has been done to investigate the prediction of RUL using an ensemble learning method that combines prediction results from multiple learning algorithms. This research aims to introduce a new ensemble prognostics method with degradation-dependent weights. The performance of the proposed method is evaluated by the C-MAPSS data sets.