Elastodynamics based Modelling of Acoustic Emission for Earlier Bearing Damage Detection

It is crucial for many applications to detect bearing damage as early as possible to allow for scheduling of maintenance with lead times that minimize operational disruption. State of the practice is the detection of spalling but damage initiates prior to spalling as subsurface and surface cracks. Such damage is much harder to detect and to model. This study proposes a unique application of the nanofrictional Prandtl-Tomlinson model to predict macroscopic acoustic emission (AE) signals that occur at cracked interfaces under relative motion. The study integrates large deformation modelling of structures with elastodynamic simulations to investigate early AE signals generated under different bearing rotational speeds. Experimental studies are carried out to measure acoustic vibrations from metal-metal surface friction using fiber optic sensors and compared to those predicted by the model. Broad agreement of results highlights the validity of this framework.