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

被引:26
作者
Jiang, Guang-Jun [1 ,2 ]
Yang, Jin-Sen [1 ,2 ]
Cheng, Tian-Cai [1 ,2 ,3 ]
Sun, Hong-Hua [1 ,2 ]
机构
[1] Inner Mongolia Univ Technol, Sch Mech Engn, Hohhot, Inner Mongolia, Peoples R China
[2] Inner Mongolia Key Lab Adv Mfg Technol, Hohhot, Inner Mongolia, Peoples R China
[3] Inner Mongolia Univ Technol, Sch Mech Engn, Hohhot 010050, Inner Mongolia, Peoples R China
关键词
Bayesian neural network; CNNLSTM; remaining useful life; rolling bearings; RELIABILITY-ANALYSIS;
D O I
10.1002/qre.3308
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页码:1756 / 1774
页数:19
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