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Changqing Shen / 沈长青
Changqing Shen / 沈长青
Professor, Soochow University / 苏州大学
Подтвержден адрес электронной почты в домене suda.edu.cn - Главная страница
Название
Процитировано
Процитировано
Год
Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis
X Guo, L Chen, C Shen
Measurement 93, 490-502, 2016
6202016
Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier
C Shen, D Wang, F Kong, PW Tse
Measurement 46 (4), 1551-1564, 2013
2442013
Stacked sparse autoencoder-based deep network for fault diagnosis of rotating machinery
Y Qi, C Shen, D Wang, J Shi, X Jiang, Z Zhu
Ieee Access 5, 15066-15079, 2017
2042017
A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines
X Jiang, J Wang, J Shi, C Shen, W Huang, Z Zhu
Mechanical Systems and Signal Processing 116, 668-692, 2019
1382019
Initial center frequency-guided VMD for fault diagnosis of rotating machines
X Jiang, C Shen, J Shi, Z Zhu
Journal of Sound and Vibration 435, 36-55, 2018
1342018
An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder
C Shen, Y Qi, J Wang, G Cai, Z Zhu
Engineering Applications of Artificial Intelligence 76, 170-184, 2018
1302018
Multi-scale deep intra-class transfer learning for bearing fault diagnosis
X Wang, C Shen, M Xia, D Wang, J Zhu, Z Zhu
Reliability Engineering & System Safety 202, 107050, 2020
1152020
A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions
J Zhu, N Chen, C Shen
Mechanical Systems and Signal Processing 139, 106602, 2020
1132020
A new deep transfer learning method for bearing fault diagnosis under different working conditions
J Zhu, N Chen, C Shen
IEEE Sensors Journal 20 (15), 8394-8402, 2019
1122019
Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction
W Fan, G Cai, ZK Zhu, C Shen, W Huang, L Shang
Mechanical Systems and Signal Processing 56, 230-245, 2015
1002015
Fault diagnosis of rotating machines based on the EMD manifold
J Wang, G Du, Z Zhu, C Shen, Q He
Mechanical Systems and Signal Processing 135, 106443, 2020
882020
Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis
S Tang, C Shen, D Wang, S Li, W Huang, Z Zhu
Neurocomputing 305, 1-14, 2018
712018
Deep fault recognizer: An integrated model to denoise and extract features for fault diagnosis in rotating machinery
X Guo, C Shen, L Chen
Applied Sciences 7 (1), 41, 2016
652016
Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions
Q Li, C Shen, L Chen, Z Zhu
Mechanical Systems and Signal Processing 147, 107095, 2020
632020
A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis
C Shen, Q He, F Kong, WT Peter
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of …, 2012
622012
A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines
CS Jun Zhu, Nan Chen
IEEE Transactions on Industrial Informatics, 2020
532020
An end-to-end model based on improved adaptive deep belief network and its application to bearing fault diagnosis
J Xie, G Du, C Shen, N Chen, L Chen, Z Zhu
IEEE Access 6, 63584-63596, 2018
482018
A new l0-norm embedded MED method for roller element bearing fault diagnosis at early stage of damage
X Jiang, X Cheng, J Shi, W Huang, C Shen, Z Zhu
Measurement 127, 414-424, 2018
452018
Bearing fault diagnosis via generalized logarithm sparse regularization
Z Zhang, W Huang, Y Liao, Z Song, J Shi, X Jiang, C Shen, Z Zhu
Mechanical Systems and Signal Processing 167, 108576, 2022
432022
A doppler transient model based on the laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis
C Shen, F Liu, D Wang, A Zhang, F Kong, PW Tse
Sensors 13 (11), 15726-15746, 2013
412013
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