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 | 488 | 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 | 415 | 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 | 373 | 2018 |
Deep residual learning-based fault diagnosis method for rotating machinery W Zhang, X Li, Q Ding ISA transactions 95, 295-305, 2019 | 329 | 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 | 326 | 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 | 313 | 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 | 312 | 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 | 272 | 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 | 241 | 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 | 234 | 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 | 195 | 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 | 187 | 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 | 179 | 2021 |
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 | 164 | 2020 |
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 | 162 | 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 | 151 | 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 | 142 | 2021 |
Data alignments in machinery remaining useful life prediction using deep adversarial neural networks X Li, W Zhang, H Ma, Z Luo, X Li Knowledge-Based Systems 197, 105843, 2020 | 136 | 2020 |
Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions W Zhang, X Li, H Ma, Z Luo, X Li Reliability Engineering & System Safety 211, 107556, 2021 | 133 | 2021 |
Domain generalization in rotating machinery fault diagnostics using deep neural networks X Li, W Zhang, H Ma, Z Luo, X Li Neurocomputing 403, 409-420, 2020 | 118 | 2020 |