Data-Driven Method for Predicting Remaining Useful Life of Bearings Based on Multi-Layer Perception Neural Network and Bidirectional Long Short-Term Memory Network  

作　　者：Yongfeng Tai Xingyu Yan Xiangyi Geng Lin Mu Mingshun Jiang Faye Zhang 

机构地区：[1]CRRC Qingdao Sifang Co.,Ltd.,Qingdao,266111,China [2]School of Control Science and Engineering,Shandong University,Jinan,250061,China [3]Public(Innovation)Experimental Teaching Center,Shangdong University,Qingdao,266237,China [4]Engineering Training Center,Shangdong University,Jinan,250061,China

出　　处：《Structural Durability & Health Monitoring》2025年第2期365-383,共19页结构耐久性与健康监测(英文)

基　　金：supported by the National Key Research and Development Project(Grant Number 2023YFB3709601);the National Natural Science Foundation of China(Grant Numbers 62373215,62373219,62073193);the Key Research and Development Plan of Shandong Province(Grant Numbers 2021CXGC010204,2022CXGC020902);the Fundamental Research Funds of Shandong University(Grant Number 2021JCG008);the Natural Science Foundation of Shandong Province(Grant Number ZR2023MF100).

摘　　要：The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.

关 键 词：Remaining useful life prediction rolling bearing health indicator construction multilayer perceptron bidirectional long short-term memory network 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]