Rolling bearing fault diagnosis based on BiLSM network

被引：0

作者：

Zhao Z. ^{[1
,2
]}

Zhao J. ^{[1
]}

Wei Z. ^{[1
]}

机构：

[1] School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang

[2] State Key Lab of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2021年 / 40卷 / 01期

关键词：

Bearing fault diagnosis; Bidirectional long short term memory (BiLSTM) network; Deep learning;

D O I：

10.13465/j.cnki.jvs.2021.01.013

中图分类号：

学科分类号：

摘要：

Aiming at rolling bearing fault diagnosis, a diagnosis model based on the bidirectional long short term memory (BiLSTM) network was designed and implemented. The original vibration signal was directly used as the input of the model and rolling bearing fault features were extracted automatically to do fault recognition of rolling bearings with different fault types and damage degrees of inner race, rolling element and outer race. The deep information of bearing vibration signals was extracted with BiLSTM network to make up for the deficiency of traditional fault diagnosis methods needing to extract features manually, and thus realize the end-to-end intelligent fault diagnosis of rolling bearing. The test results of rolling bearing really measured vibration signals showed that the fault recognition correctness rate of the proposed method can reach 99.8%; the proposed method has a certain application value. © 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：95 / 101

页数：6

共 24 条

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