Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault

Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault

Diogo Stuani Alves, Gregory Bregion Daniel, Helio Fiori de Castro, Tiago Henrique Machado, Katia Lucchesi Cavalca, Ozhan Gecgel, João Paulo Dias, Stephen Ekwaro-Osire

ARTIGO

Inglês

Bearings play a crucial role in machine longevity and is, at the same time, one of the most critical sources of failure in rotor dynamics. Particularly for journal bearings, it is not completely understood how specific damages may influence the response of the rotating system. Consequently, the...

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP

Fechado

Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault

Diogo Stuani Alves, Gregory Bregion Daniel, Helio Fiori de Castro, Tiago Henrique Machado, Katia Lucchesi Cavalca, Ozhan Gecgel, João Paulo Dias, Stephen Ekwaro-Osire


										

Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault

Diogo Stuani Alves, Gregory Bregion Daniel, Helio Fiori de Castro, Tiago Henrique Machado, Katia Lucchesi Cavalca, Ozhan Gecgel, João Paulo Dias, Stephen Ekwaro-Osire

    Fontes

    Mechanism and machine theory

    Vol. 149 (July, 2020), n. art. 103835