A Hybrid Fault Diagnosis Method for Rolling Bearing Based on Attention and CNN-Reference-Cited by-同舟云学术

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A Hybrid Fault Diagnosis Method for Rolling Bearing Based on Attention and CNN

Published:2023-12-01 Issue: Volume: Page:494-498
ISSN:
Container-title:2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)
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Author:

Wang Songtao¹,Liu Qianlong²,Li Jun³,Liang Kun³,Cai Hui⁴,Tian Hangwei⁵

Affiliation:

1. Shandong Hi-Speed Construction Management Group Co., Ltd.,Jinan,China

2. SHANDONG SCICOM Information and Economy Research Institute Co.,Ltd.,Jinan,China

3. Shandong Hi-Speed Group Linteng Highway Co., Ltd.,Jinan,China

4. School of Qilu Transportation, Shandong University,Jinan,China

5. Qilu University of Technology (Shandong Academy of Sciences),Key Laboratory of Computing Power Network and Information Security, Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan),Jinan,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10488913/10488921/10489399.pdf?arnumber=10489399

Reference9 articles.

1. Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016

2. An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

3. ANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults

4. Bearing fault diagnosis based on speed signal and CNN model

5. Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method

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