Changqing Shen / 沈长青

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Zhongkui Zhu Soochow UniversityProfessor of Mechanical Engineering, SOOCHOW UNIVERSITYAdresse e-mail validée de suda.edu.cn
weiguo HuangSoochow UniversityAdresse e-mail validée de suda.edu.cn
Dong WANGAssociate Professor, Shanghai Jiao Tong UniversityAdresse e-mail validée de sjtu.edu.cn
Juanjuan ShiSoochow UniversityAdresse e-mail validée de suda.edu.cn
Xingxing Jiang (江星星)Professor, Soochow UniversityAdresse e-mail validée de suda.edu.cn
Jun Wang （王俊）Professor, School of Rail Transportation, Soochow UniversityAdresse e-mail validée de mail.ustc.edu.cn
Jun Zhu / 朱军Associate Professor, School of Civil Aviation, Northwestern Polytechnical UniversityAdresse e-mail validée de nwpu.edu.cn
Chuancang Ding / 丁传仓Soochow UniversityAdresse e-mail validée de suda.edu.cn
Fang LiuDepartment of Mechanical Engineering, Anhui UniversityAdresse e-mail validée de ahu.edu.cn
Min Xia, Ph.D.Associate Professor, University of Western Ontario, CanadaAdresse e-mail validée de uwo.ca
Qi LiTsinghua UniversityAdresse e-mail validée de mails.tsinghua.edu.cn
Gaigai CaiXidian UniversityAdresse e-mail validée de xidian.edu.cn
Xiaojie GuoIBM TJ Watson Research CenterAdresse e-mail validée de gmu.edu
Shilong SUNAssociate Professor at Harbin Institute of Technology, ShenzhenAdresse e-mail validée de hit.edu.cn
Wei Fan 樊薇Jiangsu UniversityAdresse e-mail validée de my.cityu.edu.hk
Qingbo HeUSTC，SJTUAdresse e-mail validée de sjtu.edu.cn
Yongbin LiuAnhui UniversityAdresse e-mail validée de ustc.edu.cn
Chuan LIProfessor, Chongqing Technology and Business UniversityAdresse e-mail validée de ctbu.edu.cn
Tongtong YanPostDoc Position, University of Western Ontario, CanadaAdresse e-mail validée de uwo.ca
Haidong ShaoAssociate Professor, College of Mechanical and Vehicle Engineering, Hunan University,Adresse e-mail validée de hnu.edu.cn

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Changqing Shen / 沈长青

Professor, Soochow University / 苏州大学

Adresse e-mail validée de suda.edu.cn - Page d'accueil

Signal processing Data mining Fault diagnosis Mechanical engineering


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis X Guo, L Chen, C Shen Measurement 93, 490-502, 2016	843	2016
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	285	2020
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	283	2013
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	282	2020
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	260	2017
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	254	2019
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	210	2019
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	204	2018
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	193	2020
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	178	2022
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	175	2018
A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines CS Jun Zhu, Nan Chen IEEE Transactions on Industrial Informatics, 2020	159	2020
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	153	2021
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	143	2022
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	132	2020
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	126	2015
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	114	2021
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	103	2018
Central frequency mode decomposition and its applications to the fault diagnosis of rotating machines X Jiang, Q Song, H Wang, G Du, J Guo, C Shen, Z Zhu Mechanism and Machine Theory 174, 104919, 2022	101	2022
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	96	2022

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