Remaining Useful Life Prediction for Rolling Element Bearing Based on Ensemble Learning

Information fusion is becoming state-of-the-art methodology for performance assessment of engineering assets. Efficiently and smartly combining multi-source information and relevant models from the interested object, more accurate and reliable diagnostic and prognostic results regarding the object can be achieved, which are especially significant for the condition-based maintenance and prognostics and health management applications. Ensemble learning, as a typical machine learning and decision fusion method, has long been applied in the pattern recognition field and demonstrated promising performance. However, scarce applications of ensemble learning have been found for remaining useful life (RUL) predictions. RUL prediction based on ensemble learning by merging multi-piece information and dynamically updating is proposed in this paper. Specifically, multiple base learners are trained to work as one RUL estimator and weighted averaging with dynamically updated weights upon the latest condition monitoring information is employed to aggregate these RULs to form the final RUL. Rolling element bearing degradation experimental data is used to verify and validate the effectiveness of the proposed method.