《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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

Prediction method of RUL of bearings in GRU network based on Bayesian optimization

Published:2024-01-29 author:MENG Linshu, ZHANG Yinxuan, ZHANG Qi, et al. Browse: 291 Check PDF documents

Prediction method of RUL of bearings in GRU network based on

Bayesian optimization

MENG Linshu1, ZHANG Yinxuan1, ZHANG Qi1, WANG Hao2

(1.Shenyang Aircraft Design & Research Institute, The Aviation Industry Corporation of China, Shenyang 110035, China;

2.ASMPT Technology(China) Co., Ltd., Chengdu 610000, China)

Abstract: The traditional model for predicting the remaining service life of rolling bearings faces difficulties in parameter optimization. To address this issue, a Bayesian optimization based GRU network method for predicting the remaining service life of rolling bearings was proposed and experimentally validated. Taking the PHM2012 dataset as an example, the Bayesian optimization algorithm was used to optimize multiple hyperparameters of the Encoder Decoder based Gated Recurrent Unit (GRU) prediction model. First, the original data containing noise was subjected to wavelet packet processing. From the vibration mechanism and fault characteristics of rolling bearings, time-domain features were extracted. The extracted time-domain features were optimized and selected, and then input into the encoder part of the model to further extract deeper temporal features. Then, by integrating attention mechanism with the Encoder-Decoder structure, a bidirectional GRU neural network model was constructed. The Bayesian optimization method was employed to search in the high-dimensional hyperparameter space of the model. The optimal hyperparameter combination was gained. Linear transformation was incorporated into the decoder. The predicted remaining service life of the rolling bearings was gained. Finally, the entire process of model construction, training, and usage was encapsulated. A rolling bearing residual service life prediction workflow based on Bayesian optimization of the GRU network was established. The effectiveness of the method was validated through comparative experiments. The research results indicate that the GRU network based on Bayesian optimization can effectively predict the residual service life of rolling bearings. Comparing to the optimal results of the other three methods, the average prediction score of the GRU network based on Bayesian optimization improves by 8.01%. The GRU network based on Bayesian optimization demonstrates accurate predictions for rolling bearings with shorter life. For bearings with longer life, the predicted values do not exceed the real values. Therefore, it can serve as a reference for remaining useful life estimation in the approaching failure stage of the bearings.

Key words: parameters optimization; remaining useful life(RUL); gate recurrent unit(GRU); Bayesian optimization; hyper-parameters tuning; attention mechanism; Encoder-Decoder structure

Advanced Search

Online Office System

Chinese Core Periodicals
Chinese Sci-tech Core Periodicals
SA, INSPEC Indexed
CSA: T Indexed
UPD:Indexed