Rolling bearing fault diagnosis utilizing pearson’s correlation coefficient optimizing variational mode decomposition based deep learning model-Reference-Cited by-同舟云学术

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Rolling bearing fault diagnosis utilizing pearson’s correlation coefficient optimizing variational mode decomposition based deep learning model

Published:2022-08-05 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control ( SDPC)
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Author:

Hou Qilin¹,Liu Ying¹,Guo Peipei¹,Shao Chentong¹,Cao Liang¹,Huang Lan¹

Affiliation:

1. AVIC Shanghai Aero Measurement-Controlling Research Institute,Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management,Shanghai,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9915555/9915798/09915985.pdf?arnumber=9915985

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Early-Stage Fault Diagnosis of Motor Bearing Based on Kurtosis Weighting and Fusion of Current–Vibration Signals;Sensors;2024-05-24

2. Fault Diagnosis of Hyperbolic Distributed Parameter System;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17

3. Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM;Computers, Materials & Continua;2024

4. Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism;Entropy;2023-10-21

5. A model-agnostic meta-learning fault diagnosis method based on dynamic weighting;2023 IEEE PELS Students and Young Professionals Symposium (SYPS);2023-08-27

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