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

Document Type : Research Paper


1 M.Sc., Mechanical Engineering Department, Shahid Rajaei University, Tehran, Iran

2 Corresponding Author, Assistant Professor, Faculty of Mechanical Engineering Department, Shahid Rajaei University, Tehran, Iran

3 PhD Student, Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran


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 by assuming 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 new model more
applicable than previous models.


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