Frequency Domain Feature Extraction and Long Short-Term Memory for Rolling Bearing Fault Diagnosis-Reference-Cited by-同舟云学术

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Frequency Domain Feature Extraction and Long Short-Term Memory for Rolling Bearing Fault Diagnosis

Published:2022-10 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Machine Learning, Control, and Robotics (MLCR)
language:
Short-container-title:

Author:

Wang Tianhao¹,Qin Rui²,Meng Hongying¹,Li MaoZhen¹,Cheng Meng¹,Liu Yi³

Affiliation:

1. Brunel University London,Department of Electronic and Electrical Engineering,London,UK

2. Manchester Metropolitan University,Department of Computing and Mathematics,Manchester,UK

3. Beijing Institute of Graphic Communication,Department of New Media,Beijing,China

Funder

Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10036947/10036955/10037096.pdf?arnumber=10037096

Reference32 articles.

1. Nonlinear Dimensionality Reduction by Locally Linear Embedding

2. Locality preserving projections;he;Advances in neural information processing systems,2003

3. Vibration signal classification by wavelet packet energy flow manifold learning

4. Linear local tangent space alignment and application to face recognition

5. Mobile traffic forecasting using a combined FFT/LSTM strategy in SDN networks

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1. Fault Detection of Wheelset Bearings through Vibration-Sound Fusion Data Based on Grey Wolf Optimizer and Support Vector Machine;Technologies;2024-08-28

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