Multi-Time Scale Modeling Strategy for Bearing Life Prognosis

Prediction of a bearing service life is traditionally achieved by empirical or physical models, which have their own strengths and limitations. In an effort to combine the strengths of these modeling approaches, this research investigates the concept of Multi-Time Scale Modeling (MTSM). Specifically, a MTSM strategy for bearing life prognosis is developed by correlating experimentally acquired bearing vibration data with physics based model of microscopic growth of crack size. The strategy is composed of a fast scale empirical model (e.g., root mean square value of vibration), a slow scale physical model (e.g., change of crack length over one loading cycle), and a model coupling mechanism (e.g., bidirectional mapping functions). The fast and slow scale models are obtained by polynomial regression analysis and using the concept of the Paris Law, respectively. The coupling mechanism is established through introduction of dynamic mass into the model. The improvement in bearing service life prediction, obtained by the presented MTSM strategy is experimentally validated.