关注
Ruqiang Yan / 严如强
Ruqiang Yan / 严如强
UMass,UConn,SEU,CASE,XJTU
在 ieee.org 的电子邮件经过验证
标题
引用次数
引用次数
年份
Deep learning and its applications to machine health monitoring
R Zhao, R Yan, Z Chen, K Mao, P Wang, RX Gao
Mechanical Systems and Signal Processing 115, 213-237, 2019
23742019
Wavelets for fault diagnosis of rotary machines: A review with applications
R Yan, RX Gao, X Chen
Signal Processing 96, 1-15, 2014
13902014
Highly-accurate machine fault diagnosis using deep transfer learning
S Shao, S McAleer, R Yan, P Baldi
IEEE Transactions on Industrial Informatics 15 (4), 2446-2455, 2019
10572019
Machine Health Monitoring Using Local Feature-based Gated Recurrent Unit Networks
R Zhao, D Wang, R Yan, K Mao, F Shen, J Wang
IEEE Transactions on Industrial Electronics 65 (2), 1539-1548, 2018
7522018
A sparse auto-encoder-based deep neural network approach for induction motor faults classification
W Sun, S Shao, R Zhao, R Yan, X Zhang, X Chen
Measurement 89, 171-178, 2016
7422016
Learning to monitor machine health with convolutional bi-directional lstm networks
R Zhao, R Yan, J Wang, K Mao
Sensors 17 (2), 273, 2017
7232017
Wavelets: Theory and applications for manufacturing
RX Gao, R Yan
Springer Science & Business Media, 2010
6092010
Approximate entropy as a diagnostic tool for machine health monitoring
R Yan, RX Gao
Mechanical Systems and Signal Processing 21 (2), 824-839, 2007
5202007
Deep Transfer Learning Based on Sparse Auto-encoder for Remaining Useful Life Prediction of Tool in Manufacturing
C Sun, M Ma, Z Zhao, S Tian, R Yan, X Chen
IEEE Transactions on Industrial Informatics 15 (4), 2416-2425, 2019
4272019
A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
W Li, R Huang, J Li, Y Liao, Z Chen, G He, R Yan, K Gryllias
Mechanical Systems and Signal Processing 167, 108487, 2022
4212022
Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines
R Yan, Y Liu, RX Gao
Mechanical Systems and Signal Processing 29, 474-484, 2012
4082012
Generative adversarial networks for data augmentation in machine fault diagnosis
S Shao, P Wang, R Yan
Computers in Industry 106, 85-93, 2019
4062019
Hilbert–Huang transform-based vibration signal analysis for machine health monitoring
R Yan, RX Gao
IEEE Transactions on instrumentation and measurement 55 (6), 2320-2329, 2006
4002006
Long short-term memory for machine remaining life prediction
J Zhang, P Wang, R Yan, RX Gao
Journal of manufacturing systems 48, 78-86, 2018
3982018
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Z Zhao, T Li, J Wu, C Sun, S Wang, R Yan, X Chen
ISA transactions 107, 224-255, 2020
3602020
Machine Remaining Useful Life Prediction via an Attention Based Deep Learning Approach
Z Chen, M Wu, R Zhao, F Guretno, R Yan, X Li
IEEE Transactions on Industrial Electronics 68 (3), 2521- 2531, 2021
3462021
Prognosis of defect propagation based on recurrent neural networks
A Malhi, R Yan, RX Gao
IEEE Transactions on Instrumentation and Measurement 60 (3), 703-711, 2011
3182011
Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study
Z Zhao, Q Zhang, X Yu, C Sun, S Wang, R Yan, X Chen
IEEE Transactions on Instrumentation and Measurement 70, 1-28, 2021
3062021
DCNN-based Multi-signal Induction Motor Fault Diagnosis
S Shao, R Yan, Y Lu, P Wang, R Gao
IEEE Transactions on Instrumentation and Measurement 69 (6), 2658-2669, 2020
2922020
Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis
W Sun, R Zhao, R Yan, S Shao, X Chen
IEEE Transactions on Industrial Informatics 13 (3), 1350-1359, 2017
2922017
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