| Remaining useful life estimation in prognostics using deep convolution neural networks X Li, Q Ding, JQ Sun Reliability Engineering & System Safety 172, 1-11, 2018 | 1421 | 2018 |
| Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction X Li, W Zhang, Q Ding Reliability engineering & system safety 182, 208-218, 2019 | 466 | 2019 |
| Multi-layer domain adaptation method for rolling bearing fault diagnosis X Li, W Zhang, Q Ding, JQ Sun Signal processing 157, 180-197, 2019 | 396 | 2019 |
| Cross-domain fault diagnosis of rolling element bearings using deep generative neural networks X Li, W Zhang, Q Ding IEEE Transactions on Industrial Electronics 66 (7), 5525-5534, 2018 | 365 | 2018 |
| Deep residual learning-based fault diagnosis method for rotating machinery W Zhang, X Li, Q Ding ISA transactions 95, 295-305, 2019 | 313 | 2019 |
| Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism X Li, W Zhang, Q Ding Signal processing 161, 136-154, 2019 | 307 | 2019 |
| Machinery fault diagnosis with imbalanced data using deep generative adversarial networks W Zhang, X Li, XD Jia, H Ma, Z Luo, X Li Measurement 152, 107377, 2020 | 300 | 2020 |
| Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation X Li, W Zhang, Q Ding, JQ Sun Journal of Intelligent Manufacturing 31 (2), 433-452, 2020 | 298 | 2020 |
| Diagnosing rotating machines with weakly supervised data using deep transfer learning X Li, W Zhang, Q Ding, X Li IEEE transactions on industrial informatics 16 (3), 1688-1697, 2019 | 262 | 2019 |
| A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning X Li, W Zhang, Q Ding Neurocomputing 310, 77-95, 2018 | 235 | 2018 |
| Federated learning for machinery fault diagnosis with dynamic validation and self-supervision W Zhang, X Li, H Ma, Z Luo, X Li Knowledge-Based Systems 213, 106679, 2021 | 212 | 2021 |
| A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning K Yu, TR Lin, H Ma, X Li, X Li Mechanical Systems and Signal Processing 146, 107043, 2021 | 206 | 2021 |
| Universal domain adaptation in fault diagnostics with hybrid weighted deep adversarial learning W Zhang, X Li, H Ma, Z Luo, X Li IEEE Transactions on Industrial Informatics 17 (12), 7957-7967, 2021 | 183 | 2021 |
| Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places X Li, W Zhang, NX Xu, Q Ding IEEE Transactions on Industrial Electronics 67 (8), 6785-6794, 2019 | 180 | 2019 |
| Open-set domain adaptation in machinery fault diagnostics using instance-level weighted adversarial learning W Zhang, X Li, H Ma, Z Luo, X Li IEEE Transactions on Industrial Informatics 17 (11), 7445-7455, 2021 | 164 | 2021 |
| Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation W Zhang, X Li, X Li Measurement 164, 108052, 2020 | 155 | 2020 |
| Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics X Li, W Zhang IEEE Transactions on Industrial Electronics 68 (5), 4351-4361, 2020 | 153 | 2020 |
| Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks X Li, W Zhang, H Ma, Z Luo, X Li Neural Networks 129, 313-322, 2020 | 145 | 2020 |
| Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy W Zhang, X Li IEEE/ASME Transactions on Mechatronics 27 (1), 430-439, 2021 | 130 | 2021 |
| Quality analysis in metal additive manufacturing with deep learning X Li, X Jia, Q Yang, J Lee Journal of Intelligent Manufacturing 31 (8), 2003-2017, 2020 | 128 | 2020 |
Wei ZhangShenyang Aerospace University在 tju.edu.cn 的电子邮件经过验证
