Wear identification in rotor-bearing systems by measurements of dynamic bearing characteristics

During operation of rotating machines, journal and bearing mechanisms are progressively worn down. To prevent catastrophic failure of a rotating system, it is necessary both to detect wear precisely, without shutting down and dismantling the machinery and to predict future replacement needs. In this work, Computational Fluid Dynamics (CFD) analysis is used to solve the Navier–Stokes equations. Diagrams of bearing characteristics such as relative eccentricity, attitude angle, lubricant side flow and friction coefficient versus Sommerfeld number are presented for various wear depths and used for online wear identification. A graphical detection method is analytically presented to identify the wear depth associated with the measured dynamic bearing characteristics.