Changqing Shen / 沈长青

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

Stebėti

Changqing Shen / 沈长青

Professor, Soochow University / 苏州大学

Patvirtintas el. paštas suda.edu.cn - Pagrindinis puslapis

Signal processing Data mining Fault diagnosis Mechanical engineering


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis X Guo, L Chen, C Shen Measurement 93, 490-502, 2016	849	2016
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	287	2020
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	279	2013
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	262	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	211	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	199	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	194	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	177	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	165	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	156	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	141	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	134	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	123	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	117	2021
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	104	2022
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	104	2018
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	98	2022

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