Fault Diagnosis Based on Multiscale Multivariate Dispersive Entropy and Bayesian-optimized LSTM Networks for Motor Bearing-Reference-Cited by-同舟云学术

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Fault Diagnosis Based on Multiscale Multivariate Dispersive Entropy and Bayesian-optimized LSTM Networks for Motor Bearing

Published:2023-09-22 Issue: Volume: Page:
ISSN:
Container-title:2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
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Author:

Li Ling¹,Ma Yucheng¹,Li Jiaxin¹,Lan Kaixuan¹,Wang Menglong¹,Pan Zhuofu²

Affiliation:

1. Changsha University of Science and Technology,School of Electrical and Information Engineering,Changsha,China,410114

2. Central South University,School of Automation,Changsha,China,410083

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295747.pdf?arnumber=10295747

Reference24 articles.

1. Bearing Fault Feature Extraction Method Based on GA-VMD and Center Frequency

2. A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

3. Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection

4. Multivariate Multiscale Dispersion Entropy of Biomedical Times Series

5. Multi-information Fusion Fault Diagnosis Based on KNN and Improved Evidence Theory

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