Ensemble Prognostics With Degradation-Dependent Weights: Prediction of Remaining Useful Life for Aircraft Engines

The objective of this research is to introduce a new ensemble prognostics method with degradation-dependent weights. Specifically, this method assigns an optimized, degradation-dependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RUL of mechanical components with better accuracy. The ensemble prognostic algorithm is demonstrated using a data set collected from an engine simulator. Analysis results show that the predictive model trained by the ensemble learning algorithm outperform the existing methods.