Introduction

International Standard Serial Number:

ISSN 1001-4551

Sponsor:

Zhejiang University;

Zhejiang Machinery and Electrical Group

Edited by:

Editorial of Journal of Mechanical & Electrical Engineering

Chief Editor:

ZHAO Qun

Vice Chief Editor:

TANG ren-zhong,

LUO Xiang-yang

Tel:

86-571-87041360,87239525

Fax:

86-571-87239571

Add:

No.9 Gaoguannong,Daxue Road,Hangzhou,China

P.C:

310009

E-mail:

meem_contribute@163.com

Remaining useful life prediction of rolling bearings based on attention mechanism
Published:2023-06-25 author:LU Jin, ZHANG Yong-ping. Browse: 1177 Check PDF documents
Remaining useful life prediction of rolling bearings based on 
attention mechanism


LU Jin, ZHANG Yong-ping

(School of Information Engineering, Yancheng Institute of Technology, Yancheng 224002, China)


Abstract: Aiming at the problems that the existing feature extraction methods of bearing vibration signals relied too much on expert experience and the memory degradation caused by too long sequences in life prediction, a prediction model (method) for the remaining useful life (RUL) of rolling bearings was proposed by combining convolutional neural network-attention network(CNN-attention) and the Encoder-Decoder method based on the attention mechanism.First, the initial vibration signal of the rolling bearing was converted into a frequency domain amplitude signal using the fast Fourier transform (FFT) method. Then, a model based on attention mechanism was designed, in which degenerate feature extraction was performed by using CNN-attention, RUL prediction was performed by using the Encoder-Decoder network based on attention mechanism, and the problem of memory decay in the recurrent neural network in long-distance signal transmission was further solved. Finally, in order to verify the validity of the feature extraction model as well as the lifetime prediction model,the experiments were conducted with the PHM 2012 bearing degradation data set using the PRONOSTIA experimental platform for accelerated bearing degradation. And the obtained predictions were compared with the predictions without the attention mechanism model and the results of other literature methods. The experiment results show that compared with the results obtained by other methods, the average absolute errors of the method based on the attention mechanism model are reduced by 29.41%, 32.00%, 29.56%, 32.34%, and the average scores are increased by 0.39%, 0.98%, 0.82% and 15.46%. The research results show that,in terms of bearing RUL prediction, the attention mechanism-based bearing remaining useful life prediction model (method) is effective.

Key words:  remaining useful life(RUL); convolutional neural network-attention mechanism network(CNN-attention); EncoderDecoder model; degenerate feature extraction; lifetime prediction model of rolling bearing; memory degradation
Advanced Search
Online Office System
  • Chinese Core Periodicals
  • Chinese Sci-tech Core Periodicals
  • SA, INSPEC Indexed
  • CSA: T Indexed
  • UPD:Indexed


2010 Zhejiang Information Institute of Mechinery Industry

Technical Support：Hangzhou Bory science and technology

You are 1895221 visit this site