Remaining useful life prediction for train bearing based on an ILSTM network with adaptive hyperparameter optimization  

作　　者：Deqiang He Jingren Yan Zhenzhen Jin Xueyan Zou Sheng Shan Zaiyu Xiang Jian Miao 

机构地区：[1]State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures,Guangxi Key Laboratory of Manufacturing System&Advanced Manufacturing Tec hnology,Sc hool of Mec hanical Engineering,Guangxi Univ ersity,Nanning 530004,Guangxi,China [2]Zhuzhou CRRC Times Electric Co.,Ltd.,Zhuzhou 412001,Hunan,China

出　　处：《Transportation Safety and Environment》2024年第2期75-86,共12页交通安全与环境（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant No.U22A2053);Major Science and Technology Project of Guangxi Province of China(Grant No.Guike AB23075209);Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund(Grant No.21-050-44-S015);Innovation Project of Guangxi Graduate Education(Grant No.YCSW2023086).

摘　　要：Remaining useful life(RUL)prediction for bearing is a significant part of the maintenance of urban rail transit trains.Bearing RUL is closely linked to the reliability and safety of train running,but the current prediction accuracy makes it difficult to meet the re-quirements of high reliability operation.Aiming at the problem,a prediction model based on an improved long short-term memory(ILSTM)network is proposed.Firstly,the variational mode decomposition is used to process the signal,the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed com-bined with the time domain characteristics.Then,to improve learning ability,a rectified linear unit(ReLU)is applied to activate a fully connected layer lying after the long short-term memory(LSTM)network,and the hidden state outputs of the layer are weighted by attention mechanism.The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM.Finally,the ILSTM is applied to predict bearing RUL.Through experimental cases,the better perfor-mance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated,and its superiority of hyperparameters setting is demonstrated.

关 键 词：train bearing remaining useful life prediction long short-term memory(LSTM) attention mechanism Harris Hawks op-timization(HHO) 

分 类 号：U279[机械工程—车辆工程]