Prediction of Bearing Remaining Useful Life based on Mutual Information and Support Vector Regression Model

2019 The 5th International Conference on Electrical Engineering, Control and Robotics

无线电电子学;计算机科学

Sui, Wentao^1 ; Zhang, Dan^2 ; Qiu, Xiaomei^1 ; Zhang, Wei^1 ; Yuan, Lin^1

School of Mechanical Engineering, Shandong University of Technology, Zibo

255049, China^1

School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo

255049, China^2

关键词: Degradation process;    Degradation state;    Lifetime degradation;    Mechanical equipment;    Mutual informations;    Remaining useful lives;    Support vector regression (SVR);    Support vector regression models;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/533/1/012032/pdf
DOI  :  10.1088/1757-899X/533/1/012032

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In order to evaluate the degradation state of the mechanical equipment and master the information of the remaining useful life (RUL) of the bearing accurately, this paper presents a method for predicting the remaining useful life of bearings based on mutual information (MI) and support vector regression (SVR) model. The proposed method includes two steps of online and offline, the offline step is used to build a degradation model of the bearing by learning, the online step uses the degradation model to predict the remaining useful life. By analyzing the experimental data of bearing full lifetime degradation, the results show that the method can effectively simulate the bearing degradation process and predict the remaining useful life of the bearing.