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Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks by Basheer Shaheen; Ádám Kocsis; István Németh is a Computer Science article available to read on EtoBox.

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It is necessary to develop advanced fault prognostics techniques for improving maintenance planning and scheduling to avoid unexpected shutdowns, additional expenses and decreased productivity. Such techniques include advanced failure prediction and remaining useful life (RUL) estimation of mechanical components comprising manufacturing systems with complicated structures. This study proposes a novel data-driven prognostic analysis approach for predicting the failure of a mechanical component based on its degradation path and estimating the RUL. A simulated labelled degradation dataset of a mechanical component with a predefined failure threshold was exploited. In order to increase the ability to maintain the increasing trend and the monotony of the degradation path, supervised machine learning models, including combined artificial neural network architectures and an improved version of the neuron-by-neuron training algorithm using accumulative neural networks design were applied for the prediction process. The expected degradation path was extrapolated as a testing dataset of the trained prediction model using an accumulative function. The predicted values are updated with each ne

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Author

Basheer Shaheen; Ádám Kocsis; István Németh

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)