A Simulation-Driven Deep Learning Approach for Condition Monitoring of Hydrodynamic Journal Bearings. Part I: Diagnostics of Wear Faults

D S Alves, T H Machado, K L Cavalca, Ozhan Gecgel, Joao Paulo Dias, Stephen Ekwaro-Osire

Research output: Contribution to conferencePaperpeer-review

Original languageEnglish
StatePublished - Oct 2019

Fingerprint Dive into the research topics of 'A Simulation-Driven Deep Learning Approach for Condition Monitoring of Hydrodynamic Journal Bearings. Part I: Diagnostics of Wear Faults'. Together they form a unique fingerprint.

Cite this