ABSTRACT
We present a method based on heterogeneous ensemble learning for the prediction of the Remaining Useful Life (RUL) of cutting tools (knives) used in the packaging industry. Ensemble diversity is achieved by training multiple prognostic models using different learning algorithms. The combination of the outcomes of the models in the ensemble is based on a weighted averaging strategy, which assigns weights proportional to the individual model performances on patterns of a validation set. The proposed heterogeneous ensemble has been applied to real condition monitoring knife data. It has provided more accurate RUL predictions compared to those of each individual base model.
