Remaining Useful Life Prediction Of Rolling Element Bearings Based On Unscented Kalman Filter

A data-driven methodology is considered in this paper focusing towards the Remaining Useful Life (RUL) prediction. Firstly, diagnostic features are extracted from training data and an analytical function that best approximates the evolution of the fault is determined and used to learn the parameters of an Unscented Kalman Filter (UKF). UKF is based on the recursive estimation of the Classic Kalman Filter (CKF) and the Unscented Transform, presenting advantages over the Extended Kalman Filter (EKF) for high non-linear systems. The learned UKF is further applied on testing data in order to predict the RUL under different operating conditions. The influence of the starting point of the prediction is analyzed and a method for the automated parameter tuning of the Kalman Filter is considered. In the end, the result is evaluated and compared to CKF and EKF on experimental data based on dedicated performance metrics.