A Data-Driven Approach for Bearing Fault Prognostics

Bearing is a critical component widely used in rotary machines. Bearing failure can cause damages of other components and lead to a lengthy downtime of the machine and costly maintenance. To reduce the cost and downtime for maintenance of the machines, it is desirable to perform fault prognostics to enable predictive health management for bearings. This paper proposes a new data-driven approach for bearing fault prognostics, which is based on the Kolmogorov-Smirnov test, self-organizing map, and unscented Kalman filter (UKF). The proposed approach has two steps. The first step is to detect bearing's degradation process by learning the historical data and the second step is to predict the remaining useful life (RUL) with the aid of a degradation model and the UKF. The proposed approach is validated by bearing's life data obtained from a run-to-failure experiment. Results show that the proposed approach can detect the bearing degradation process successfully and predict the RUL effectively.