关注
Changqing Shen / 沈长青
Changqing Shen / 沈长青
Professor, Soochow University / 苏州大学
在 suda.edu.cn 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis
X Guo, L Chen, C Shen
Measurement 93, 490-502, 2016
7892016
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
2752013
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
2532017
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
2382020
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
2352020
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
2212019
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
1942019
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
1842018
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
1672018
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
1652020
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
1482022
A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines
CS Jun Zhu, Nan Chen
IEEE Transactions on Industrial Informatics, 2020
1272020
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
1212015
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
1172020
Adversarial domain-invariant generalization: A generic domain-regressive framework for bearing fault diagnosis under unseen conditions
L Chen, Q Li, C Shen, J Zhu, D Wang, M Xia
IEEE Transactions on Industrial Informatics 18 (3), 1790-1800, 2021
1102021
Fully interpretable neural network for locating resonance frequency bands for machine condition monitoring
D Wang, Y Chen, C Shen, J Zhong, Z Peng, C Li
Mechanical Systems and Signal Processing 168, 108673, 2022
1012022
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
942018
An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis
X Jiang, J Wang, C Shen, J Shi, W Huang, Z Zhu, Q Wang
Structural Health Monitoring 20 (5), 2708-2725, 2021
922021
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
892016
Multisource domain feature adaptation network for bearing fault diagnosis under time-varying working conditions
R Wang, W Huang, J Wang, C Shen, Z Zhu
IEEE Transactions on Instrumentation and Measurement 71, 1-10, 2022
742022
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