A Data-Driven Approach for Bearing Fault Prognostics

Abstract

Bearings are one of the critical components widely used in rotary machines. Bearing failure can be catastrophic and may lead to a lengthy downtime of systems for maintenance. Bearing fault prognostics can help reduce the cost for maintenance and avoid catastrophic failures of the systems. 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.