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

Author(s)

Gecgel, Ozhan ^[1] ; Dias, Joao Paulo ^[2] ; Ekwaro-Osire, Stephen ^[1] ; Alves, Diogo Stuani ^[3] ; Machado, Tiago Henrique ^[3] ; Daniel, Gregory Bregion ^[3] ; de Castro, Helio Fiori ^[3] ; Cavalca, Katia Lucchesi ^[3]

Affiliation

^[1] Texas Tech Univ, Dept Mech Engn, Lubbock, TX 79409 - USA

^[2] Shippensburg Univ Penn, Dept Civil & Mech Engn, Shippensburg, PA 17257 - USA

^[3] Univ Estadual Campinas, Sch Mech Engn, BR-13083970 Campinas, SP - Brazil

• Document type: Journal article
• Source: JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME; v. 143, n. 8 AUG 2021.
• Web of Science Citations: 0

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. (AU)

• FAPESP's process: 15/20363-6 - Fault tolerant identification and control of rotating systems
• Grantee: Katia Lucchesi Cavalca Dedini
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/21581-5 - Experimental evaluation of a fault model for wear in hydrodynamic bearings.
• Grantee: Diogo Stuani Alves
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 19/00974-1 - Condition monitoring and prognostics of bearings considering uncertainties
• Grantee: Katia Lucchesi Cavalca Dedini
• Support Opportunities: Regular Research Grants