An integrated Bayesian approach to prognositics of the remaining useful life and its application on bearing degradation problem

Degradation information of a complex mechanical system reflects the system's health status and is useful to predict the future progression of the fault or anomalous behaviors. This paper proposed a two-stage strategy to predict the future health status of a bearing by utilizing the bearing's degradation information. The first stage was implemented to monitor the bearing's health status until a degradation point was detected. When the bearing begins to degrade, a prediction stage based on Kalman filter was then used to estimate the remaining useful life (RUL) of the bearing. Finally, a real bearing degradation problem was used to verify our method.