A Muti-stage Selection Filter Based on Wavelet Packet and 2DCNN for Fault Diagnosis of Rotating Machinery-Reference-Cited by-同舟云学术

A Muti-stage Selection Filter Based on Wavelet Packet and 2DCNN for Fault Diagnosis of Rotating Machinery

Published:2023-07-24 Issue: Volume: Page:
ISSN:
Container-title:2023 42nd Chinese Control Conference (CCC)
language:
Short-container-title:

Author:

He Wenbin¹,Mao Jianxu¹,Liu Li²,Li Zhe¹,Yang Miao³,Wang Yaonan¹

Affiliation:

1. College of Electrical and Information Engineering, Hunan University,Changsha,China,410082

2. National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University,Changsha,China,410082

3. State Grid Hunan Electric Power Company Limited Research Institute,Changsha,China,410007

Funder

National Key Research & Development Program of China

Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10239690/10239711/10240667.pdf?arnumber=10240667

Reference22 articles.

1. Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery

2. Shock detection of rotating machinery based on activated time-domain images and deep learning: An application to railway wheel flat detection

3. Fault Diagnosis of Rolling Bearing based on VMD Feature Energy Reconstruction and ADE-ELM

4. Noise-robust machinery fault diagnosis based on self-attention mechanism in wavelet domain;tian;Measurement,2022

5. Enhanced Discriminate Feature Learning Deep Residual CNN for Multi-task Bearing Fault Diagnosis with Information Fusion;niu;IEEE Trans on Industrial Informatics,2022

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1. Multi-scale quaternion CNN and BiGRU with cross self-attention feature fusion for fault diagnosis of bearing;Measurement Science and Technology;2024-05-29

2. Fault Identification of Rotating Machinery Based on Dynamic Feature Reconstruction Signal Graph;IEEE/ASME Transactions on Mechatronics;2023