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A heterogeneous ensemble approach for the prediction of the remaining useful life of packaging industry machinery

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 modelsin the ensembleis based on a weighted averaging strat-egy,which assigns weights proportional to the individual model performances on patterns of a vali-dation 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.
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  • HAL Id : hal-01989080, version 1

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Francesco Cannarile, Piero Baraldi, M. Compare, D. Borghi, L. Capelli, et al.. A heterogeneous ensemble approach for the prediction of the remaining useful life of packaging industry machinery. 2018 European Safety and Reliability Conference (ESREL 2018), Jun 2018, Trondheim, Norway. pp.87-92. ⟨hal-01989080⟩

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