A Comprehensive Review of Rolling Bearing Life Prediction: From Fatigue Life Model to Data-Driven Remaining Useful Life Prognostic
Abstract
Rolling bearings serve as core rotating components in high-end equipment such as aerospace systems, wind turbines, and high-speed electric multiple units, and their service life directly affects the operational reliability and service life of the host machinery. To clarify the research landscape of rolling bearing life prediction, summarize existing prediction techniques, and identify future development trends, this paper systematically reviews the major research advances in this field. The review first traces the evolution of bearing life models, with particular emphasis on the roles of key influencing factors, including stress thresholds, material defects, and lubrication conditions, in their development. Second, it presents a comparative analysis between conventional life calculation methods and those that account for dynamic variations in lubrication conditions, thereby revealing the influence patterns and underlying mechanisms through which surface topography and oil-film characteristics affect fatigue life. Third, it discusses methods for assessing bearing system life, with special attention given to accelerated life testing techniques and bearing condition monitoring approaches. Finally, it summarizes the state of the art in data-driven bearing life prediction and identifies online sensing of lubrication states, system-level reliability design, and improvements in the interpretability and robustness of AI-based prediction models as important future research directions in rolling bearing life prediction.