| Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis X Guo, L Chen, C Shen Measurement 93, 490-502, 2016 | 815 | 2016 |
| 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 | 282 | 2013 |
| 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 | 255 | 2020 |
| 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 | 255 | 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 | 254 | 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 | 239 | 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 | 199 | 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 | 192 | 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 | 182 | 2020 |
| 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 | 169 | 2018 |
| 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 | 161 | 2022 |
| A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines CS Jun Zhu, Nan Chen IEEE Transactions on Industrial Informatics, 2020 | 144 | 2020 |
| 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 | 130 | 2022 |
| 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 | 127 | 2021 |
| 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 | 124 | 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 | 122 | 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 | 105 | 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 | 99 | 2018 |
| 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 | 90 | 2016 |
| 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 | 86 | 2022 |
Zhongkui Zhu Soochow UniversityProfessor of Mechanical Engineering, SOOCHOW UNIVERSITY在 suda.edu.cn 的电子邮件经过验证
