Simulation-Driven Deep Learning Approach for Wear Diagnostics in Hydrodynamic Journal Bearings

Ozhan Gecgel, João Paulo DIas, Stephen Ekwaro-Osire, DIogo Stuani Alves, Tiago Henrique MacHado, Gregory Bregion Daniel, Helio Fiori De Castro, Katia Lucchesi Cavalca

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Early diagnosis in rotating machinery has been a challenge when looking toward the concept of intelligent machines. A crucial and critical component in these systems is the lubricated journal bearing, subjected to wear fault by abrasive removing of material in its inner wall, mainly during run-ups and run-downs. In extreme conditions, wear faults can cause unexpected shutdowns in rotating systems. Consequently, advanced condition monitoring is an essential procedure in the wear diagnosis of journal bearings. Although an increasing number of data-driven condition monitoring approaches for rotating machines have been proposed in the past decade, they mostly rely on substantial amounts of experimental data for training, which is expensive and time-consuming to obtain. The objective of this work is to develop a framework using a deep learning algorithm to classify wear faults in hydrodynamic journal bearings using simulated vibrations signals. Numerically simulated data sets under different wear severity levels and operating conditions were used to train and test the diagnostics framework. The results show that the proposed framework can be a promising tool to diagnose wear faults in journal bearings.

Original languageEnglish
Article number084501 EN
JournalJournal of Tribology
Volume143
Issue number8
DOIs
StatePublished - Aug 1 2021

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