Remaining Useful Life Prediction of Rolling Bearings Based on Recurrent Neural Network

Abstract

In order to acquire the degradation state of rolling bearings and achieve predictive maintenance, this paper proposed a novel Remaining Useful Life (RUL) prediction of rolling bearings based on Long Short Term Memory (LSTM) neural net-work. The method is divided into two parts: feature extraction and RUL prediction. Firstly, a large number of features are extracted from the original vibration signal. After correlation analysis, the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model. In the part of RUL prediction, LSTM that making full use of the network’s memory in time is used to improve the accuracy of RUL prediction. The proposed method is validated by life cycle experimental data of bearings, and the RUL prediction results of LSTM model are compared with Support Vector Regression (SVR) and Light Gradient Boosting Machine (LightGBM) models respectively. The results show that the proposed method is more suitable for RUL prediction of rolling bearings.