Remaining useful life prediction of rolling bearings based on Bayesian neural network and uncertainty quantification

This paper constructs a remaining useful life (RUL) prediction model combining a convolutional neural network and a long short‐term memory network (CNNLSTM) to support decision‐making, especially the safety of rotational equipment. It avoids the influence of personnel and realizes the complementary...

Full description

Saved in:
Bibliographic Details
Published in:Quality and reliability engineering international 2023-07, Vol.39 (5), p.1756-1774
Main Authors: Jiang, Guang‐Jun, Yang, Jin‐Sen, Cheng, Tian‐Cai, Sun, Hong‐Hua
Format: Article
Language:English
Subjects:
Artificial neural networks
Bayesian analysis
Bayesian neural network
CNNLSTM
Life prediction
Prediction models
remaining useful life
Roller bearings
rolling bearings
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper constructs a remaining useful life (RUL) prediction model combining a convolutional neural network and a long short‐term memory network (CNNLSTM) to support decision‐making, especially the safety of rotational equipment. It avoids the influence of personnel and realizes the complementary advantages of the network. With the assistance of Bayesian short‐term and long‐term memory neural networks, the remaining life prediction method is able to provide the confidence interval of the remaining life prediction of rolling bearings. The compression between the proposed method and existing state‐of‐the‐art methods validated the good performance of the proposed method. Overall, the proposed method contributes to life prediction and condition‐based maintenance of bearings and complex rotational systems.
ISSN:0748-8017
1099-1638
DOI:10.1002/qre.3308