Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault-Reference-Cited by-同舟云学术

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

Published:2020-07 Issue: Volume:149 Page:103835
ISSN:0094-114X
Container-title:Mechanism and Machine Theory
language:en
Short-container-title:Mechanism and Machine Theory

Author:

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

Funder

São Paulo Research Foundation

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Mechanics of Materials,Bioengineering

Reference31 articles.

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2. Numerical prediction of cavitation in noncircular journal bearings;Vaidyanathan;Tribol. Trans.,1989

3. An investigation of the performance of a journal bearing with a slightly irregular bore;Crosby;Tribol. Int.,1992

4. Thermal analysis of noncircular bearings;Hussain;J. Tribol.,1996

5. On 2xrev - vibration components in rotating machinery excited by journal ovalization and oil film non-linearity;Bachschmid,2000

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