A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine  

作　　者：Sen-Hui Wang Xi Kang Cheng Wang Tian-Bing Ma Xiang He Ke Yang 

机构地区：[1]School of Mechatronics Engineering,Anhui University of Science and Technology,Huainan,232001,China [2]Institute of Energy,Hefei Comprehensive National Science Center,Hefei,230031,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第8期1405-1427,共23页工程与科学中的计算机建模（英文）

基　　金：supported by the Anhui Provincial Key Research and Development Project(202104a07020005);the University Synergy Innovation Program of Anhui Province(GXXT-2022-019);the Institute of Energy,Hefei Comprehensive National Science Center under Grant No.21KZS217;Scientific Research Foundation for High-Level Talents of Anhui University of Science and Technology(13210024).

摘　　要：Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

关 键 词：Bearing degradation remaining useful life estimation RReliefF feature selection extreme learning machine 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]