Advancing rolling bearing RUL estimation with a physically grounded holistic approach

Rolling element bearings (REBs) are crucial components in mechanical systems. These bearings are prone to faults such as spall formation, a surface fatigue failure caused by periodic loading cycles. While many methods detect spall formation and location, significant gaps remain in estimating spall size and predicting a bearing's remaining useful life (RUL). These gaps limit the potential to fully utilize REBs throughout their entire lifespan. This study proposes a cohesive novel approach that incorporates several physics-based models to address the challenges of the spall prognosis. The proposed models are complemented by experimental methods, like fiber Bragg grating (FBG) sensors for strain measurement. The approach includes oil debris monitoring (ODM) model, which relates cumulative mass loss of the bearing to its defect severity and can be used as validation for new CIs or algorithms. Additionally, a strain model calculates spall size by analyzing the strain induced by the ball's interaction with faulty races. The proposed framework introduces an innovative and integrated methodology for detecting spalls in bearings and estimating their RUL. By focusing on the physical properties that govern spall propagation in real-world applications, this approach helps bridge the gaps in existing knowledge and methods.