Prediction of the Remaining useful Life of the Rolling Element Bearings using Recurrent Neural Network

In this paper, the temperature feature was employed to track down the degradation trend of rolling element bearings. The remaining useful life(RUL) of the rolling element bearing was predicted byassuming root mean square growth (RMS) of the acceleration signal to exponential function form and extraction of two other features. Then, the performance of these features was investigated in the prediction using a recurrent neural network(RNN). The experimental data of the accelerated life test on the rolling element bearing have been extracted from the prognostic. Contrary to the previous works, this paper considers the temperature feature instead of the time feature and also assuming the RMS of the acceleration signal to the exponential function form and using a RNN which causes a newmodel more applicable than previous models.