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 

作者机构：CRRC Qingdao Sifang Co.Ltd.Qingdao266111China School of Control Science and EngineeringShandong UniversityJinan250061China Public(Innovation)Experimental Teaching CenterShangdong UniversityQingdao266237China Engineering Training CenterShangdong UniversityJinan250061China 

出 版 物：《Structural Durability & Health Monitoring》 (结构耐久性与健康监测(英文))

年 卷 期：2025年第19卷第2期

页      面：365-383页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

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) 

主　　题：Remaining useful life prediction rolling bearing health indicator construction multilayer perceptron bidirectional long short-term memory network 

摘      要：The remaining useful life prediction of rolling bearing is vital in safety and reliability *** engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life *** 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 *** address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is ***,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is ***,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 ***,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 *** verification experiments and comparative experiments are carried out on the XJTU-SY bearing open *** research results indicate that this method has an excellent ability to predict future HI and remaining life.