Bearing prognostics and health management based on hybrid physical mechanism and data models: a systematic review

Abstract Traditional physical models face significant challenges in parameter determination and adapting to complex systems, whereas data-driven models are constrained by data quality and quantity, making them susceptible to overfitting or underfitting problems. These limitations lead to deficiencies in robustness, physical interpretability, and generalization capabilities of existing models. In recent years, the fusion of physical mechanism-based and data-driven approaches has effectively addressed the shortcomings of both types of models, attracting widespread attention. However, there is no systematic review specifically on bearing prognosis and health management under hybrid physical mechanism and data-driven methods. To fill this gap, this paper comprehensively analyzes the research advancements in bearing prognosis and health management based on hybrid physical mechanism and data-driven methods. From the perspective of fusion strategy, the paper categorizes bearing prognosis and health management methods based on the fusion of physical mechanism and data-driven model into three levels: data level, network level, and model level, and further subdivides the research methods at each fusion level. In each subdivision field, this paper discusses the application of each research method in three main aspects: condition monitoring, fault diagnosis, and remaining useful life prediction, summarizing the research methods employed by current scholars. Finally, this paper evaluates the advantages and disadvantages of each analytical method in practical applications, identifies current research challenges, and proposes future research directions. The aim is to provide guidance and in-depth insights for researchers and engineers in the field of bearing prognosis and health management.